AI-Enabled Digital Twin Framework for Coupled Surface–Subsurface Hydrologic and Biogeochemical Simulation in the Northeast U.S. Coastal Region (Genesis Mission Award)

AI-Enabled Digital Twin Framework for Coupled Surface–Subsurface Hydrologic and Biogeochemical Simulation in the Northeast U.S. Coastal Region (Genesis Mission Award) is sponsored by U.S. Department of Energy (DOE). This opportunity supports mission-aligned projects and measurable outcomes.

Foundational AI Investments Physics-Informed Multi-Fidelity Neural Operators for Rapid Multi-Physics Simulation of Enhanced Geothermal Systems (award details ) GOLD-FLOW: Geometry-aware Operator Learning for Design of Flow Systems ( award details ) STEAM: Smart Twin for Electrode/Electrolyte Advanced Manufacturing for Fuel Cells and Electrolyzers ( award details ) LEAP-FWD: Low-latency Embedded AI for Physics—From Waveforms to Discovery ( award details ) AI-Driven Co-Design of Protein-Van der Waals Hybrids for High-Performance Microelectronics ( award details ) Drift-Aware Initialization of Coupled Earth System Models for Scalable Sub-Seasonal-to-Seasonal Prediction Using Agentic AI ( award details ) WASTECHAT: The First Tool Towards a Domain Foundational Model for DOE-EM ( award details ) PORTUS: Automatic Performance Portability for Distributed Scientific Workflows on the Genesis Mission Platform ( award details ) AI-Agentic Workflows to Advance Predictive Understanding of Fracture-Dominated Subsurface Energy Systems ( award details ) AI-Driven Physics-Based Digital Twins for In Situ Uranium Mining Optimization ( award details ) EARL: Earth-Atmosphere Agentic Research and Learning—Physics-Constrained AI Closure Development for Cloud Microphysics and Turbulence ( award details ) AI-Guided Model-Experiment Framework for Predicting Water Availability in Energy Systems Using Multi-Fidelity Watershed Modeling ( award details ) Autonomous Quantum Amplifier Workflow Optimization and Learning Framework (AQUA-WOLF) ( award details ) Al-4 Algae—Unleashing Domestic Production of Algal Biomass through Physics-Informed Machine Learning Bioreactors ( award details ) Accelerating Electromagnetic Transient Analysis through Physics-Informed Scalable Artificial Intelligence Models for Rapid Load Growth ( award details ) AI-Enabled Multi-Reference Electrochemical Digital Twin for Autonomous Molten Salt Reactor Operations ( award details ) VITA-SCALE: Vitrification AI for Scale-Bridging Prediction and Process Translation ( award details ) AI-Accelerated Exploration of Droplet Collision-Coalescence Using Observation-Constrained, Multiscale Modeling of a Turbulent-Convection Cloud Chamber ( award details ) AI-Enabled Digital Twin for Scalable Injector Optimization and Control ( award details ) From Molecules to Precipitation: Machine Learning Bridges Aerosol Microphysics, Atmospheric Turbulence, and Cloud Formation ( award details ) Latent Representation Learning for Understanding Ice Microphysical and Precipitation Processes Constrained by Radar and In Situ Observations ( award details ) AI-Driven Co-Optimization of Closed Fuel Cycles: Balancing Economics, Security, and Waste ( award details ) PHOCUS: PHage-Host Interaction Programming for Anaerobic Microbiome Control Using AI-Guided Design ( award details ) IMAGINE-AI: Integrated Microbiome Analytic Generator of Interaction Network Evolution ( award details ) Generative AI-Enabled Digital Twins for Subsurface Fracture Systems with Active Learning Multiphysics Data Assimilation ( award details ) SCOPE: Salt Chemistry Optimization and Prediction Engine for Nuclear Waste Treatment Planning ( award details ) Upscaling Particle-resolved Aerosol-Cloud Microphysics with Generative Modeling ( award details ) AI-Powered Frameworks to Enhance Grid Resilience Under High-Dimensional Uncertainties ( award details ) AI Driven Workflow with Cyber Assured Digital Twin for Autonomous Operations in Small Modular Reactors and Microreactors ( award details ) Cloud Microphysics Multi-Scale Modeling Moonshot—Creating the Next Generation Of Uncertainty-Aware Cloud Models By Leveraging Multi-Fidelity AI and DOE Observation ( award details ) GENESIS-AR: Generative Models and New Observations for Improved Season-to-Season Prediction of Atmospheric Rivers ( award details ) Multi-Agent Inverse Design of Block Polypeptoids Into Hierarchical Nanomaterials ( award details ) AI-Enabled Single Cell Phenotyping to Advance Biomanufacturing ( award details ) An AI-Enabled Digital Twin Framework for Coupled Surface-Subsurface Hydrologic and Biogeochemical Simulations in the Northeast U.S. Coastal Region ( award details ) DL4MCS: Deep Learning Methods to Enhance Sub-Seasonal Predictions of Mesoscale Convective Systems by Physics-based Systems ( award details ) Agentic AI-Driven Discovery of Enzyme Conformational Dynamics in Microbial Systems ( award details ) Bridging Resolution Disparities In The Genesis Of Mixed-Phase Clouds (BRIDGE) ( award details ) From Chip to Chiller: Verifiable Edge AI Agents for Data Center Thermal Management ( award details ) Multimodal AI Finders for Rare Earth Element Deposits: Texas, the Colorado Mineral Belt, and the Southwest United States ( award details ) AI-Accelerated Discovery of Multiscale Subsurface Architecture and Coupled Hydrobiogeochemical Processes ( award details ) AI-Orchestrated Multimodal Platforms for Accelerated Discovery, Scale-Up, and Deployment of 2D Materials ( award details ) RIVER-AI: Reservoir-Groundwater Interactions for River Flow Variability, Energy, and Resilience with AI ( award details ) 1.

Physics-Informed Multi-Fidelity Neural Operators for Rapid Multi-Physics Simulation of Enhanced Geothermal Systems Lead institution: Pacific Northwest National Laboratory (PNNL) Vision.

We envision deep subsurface energy reservoirs in fractured rock, such as enhanced geothermal systems (EGS), being sensed and actively controlled in real time, transforming today’s slow, post-hoc geophysical interpretations into decisions that safely steer fracture‑network evolution while minimizing induced‑seismicity risk. Challenge and alignment.

Controlling fractures in the deep subsurface is critical to realizing EGS and other strategic subsurface assets, yet interpreting seismic and electrical resistivity tomography (ERT) data in terms of fracture aperture, stiffness, connectivity, and permeability is intractable on operational timescales.

Coupled thermal–hydrologic–mechanical–chemical plus geophysical simulations are required to disentangle overlapping signals, but their computational cost on high‑performance computing (HPC) precludes real‑time use. AI-enabled workflow.

We will train multi‑fidelity DeepONet neural operators as physics‑informed emulators that map fracture‑network scenarios to seismic and ERT responses, combining a small number of high‑fidelity PFLOTRAN/SPECFEM simulations with larger ensembles of low‑fidelity runs.

An adaptive active‑learning loop, driven by conjugate‑kernel approximations to the neural‑tangent kernel for uncertainty quantification, selects simulations that maximally reduce emulator uncertainty per node‑hour. Variational autoencoder‑based ERT encoders and dissimilarity‑trajectory seismic encoders compress monitoring data into latent spaces, ensuring the operator learns diagnostic signal structure rather than raw waveforms.

Phase I objectives and performance targets.

We will 1) develop an on‑demand function that assembles PFLOTRAN/SPECFEM inputs, executes on HPC, and returns latent‑space‑encoded seismic and ERT responses; 2) bootstrap on random high‑fidelity scenarios over the EGS testbed geometry at the Sanford Underground Research Laboratory, then extend via CK‑UQ‑guided multi‑fidelity sampling with hyperparameter tuning; and 3) benchmark emulator fidelity on specific held‑out scenarios to determine suitability as the forward simulator for Phase II real‑time joint inversion.

We target ≥100× wall‑clock speedup by Month 6 (≥1,000× by Month 9) relative to a ~500‑node‑hour‑per‑scenario baseline, ≤1 GPU‑second per emulator call, and ≥10× reduction in required high‑fidelity simulations. Expected impact and broader relevance.

The resulting emulators, encoders, datasets, and scaling analyses will reduce forward‑model compute cost by orders-of-magnitude, quantify accuracy versus high‑fidelity data budget, and clarify where seismic and ERT are complementary or degenerate.

These products will be released to the American Science Cloud under BSD‑3‑Clause with FAIR metadata and will serve as plug‑and‑play surrogates for inversion, design‑of‑experiment, and digital‑twin workflows across Genesis subsurface teams.

The framework is directly transferable to carbon storage, nuclear‑waste isolation, and critical‑mineral recovery, establishing a general paradigm for real‑time, physics‑faithful control of fractured‑rock subsurface systems. 2.

GOLD-FLOW: Geometry-aware Operator Learning for Design of Flow Systems GOLD-FLOW is a closed-loop geometry-aware generative AI framework for the integrated design and optimization of critical fluid flow components in energy systems.

Its vision is to transform flow system component design from a slow, computational fluid dynamics (CFD)-driven process into an autonomous AI design workflow that can generate, evaluate, and optimize non-parametric geometries for U.S. Department of Energy (DOE)-relevant energy systems.

GOLD-FLOW aligns with the Topic 21-B emphasis on AI-driven design and control by improving the performance of energy systems in which component geometry, operating conditions, and flow environments are strongly coupled. Current design workflows are informed by repeated high‑fidelity CFD simulations and therefore cannot efficiently explore large free-form geometry space or co-design with operating and control variables.

GOLD-FLOW addresses this bottleneck through a unified architecture that couples generative geometry modeling, geometry-aware operator learning, and latent-space design optimization in a single closed-loop workflow.

At its core, GOLD-FLOW uses ArGEnT, a cross-attention, transformer-based geometry-aware neural operator, as the predictive engine for arbitrary geometries, and LION, a latent point diffusion model, as the generative engine for non-parametric point-cloud designs.

These components are connected through a common geometry representation and application-specific optimization objectives constructed from predicted quantities of interest, such as flow system pressure loss, turbine performance, load constraints, and nozzle efficiency.

This enables candidate designs to be generated, evaluated, and refined within one AI design loop rather than through separate manual simulation stages, while selected CFD or experimental validation provides uncertainty-gated verification.

In Phase I, GOLD-FLOW will pursue four objectives: Deploy a graphics processing unit-native flow-optimization framework using NVIDIA PhysicsNeMo Establish a unified design loop for generative geometry synthesis, quality of life prediction, and latent-space optimization Optimize redox flow battery flow-field and electrode architectures Demonstrate turbomachinery design and control for cross-flow turbines and geothermal turbine nozzle designs.

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STEAM: Smart Twin for Electrode/Electrolyte Advanced Manufacturing for Fuel Cells and Electrolyzers Smart Twin for Electrode/Electrolyte Advanced Manufacturing (STEAM) will transform solid oxide fuel cell/solid oxide/electrolyzer fuel cell (SOFC)/SOEC manufacturing from an empirical, inspect-after-failure process into a predictive, AI-enabled workflow for real-time quality prediction, process recommendation, and early defect rejection.

The project supports the Genesis mission by developing a reusable manufacturing digital twin that integrates experiments, simulation, and operational data to accelerate manufacturing innovation. STEAM focuses on tape casting, the most consequential and least understood step in SOFC/SOEC fabrication, where defects introduced early often remain undetected until costly downstream processing or final electrochemical full cell/stack testing.

The scientific and technical challenge is that tape quality and downstream cell performance emerge from nonlinear, strongly coupled interactions among powder characteristics, slurry formulation, rheology, drying behavior, and machine settings. These relationships span multiple length scales, are only partially observable during processing, and cannot be reliably captured by physics-only models or conventional empirical optimization.

Existing quality assurance/quality control methods are limited to simple measurements such as thickness and pinhole detection and do not provide actionable prediction of fired microstructure or electrochemical performance.

STEAM will use AI to enhance the manufacturing workflow by fusing three complementary data sources into a unified predictive framework: PNNL's 25+ year SOFC/SOEC fabrication dataset with 500+ records linking materials, process conditions, tape properties, microstructure, and electrochemical performance 3,000+ multi-physics finite-element simulations of tape-casting behavior new sensor-enhanced tape-casting experiments with synchronized in situ measurements.

A hierarchical transformer-based multimodal model will learn latent process “structure” performance relationships across heterogeneous and partially missing data, estimate hidden manufacturing states, predict green and fired tape quality with uncertainty, and connect intermediate tape states to downstream cell performance.

In Phase I, STEAM targets reducing the relative errors of the prediction to <15%, a reduced sensor suite retaining ~80% of full-model performance, and successful inverse recommendations for at least two quality objectives. 4.

LEAP-FWD: Low-latency Embedded AI for Physics—From Waveforms to Discovery The discovery of neutrinoless double-beta decay would provide irrefutable evidence of physics beyond the Standard Model, revealing the Majorana nature of the neutrino and providing insight into the matter-antimatter asymmetry of the universe.

The LEGEND (Large Enriched Germanium Experiment for Neutrinoless Double Beta Decay) program has the highest funding priority for U.S.‑based experiments aimed at discovery of this strongly motivated signature of new physics.

LEGEND operates an array of high-purity germanium detectors in combination with various integrated active veto systems immersed in a liquid argon environment, enabling an ultra-low radioactivity setting for optimal background discrimination.

The scientific reach of the program and its ability to probe rare signals across a broad range of new physics can be further improved through implementation of a new AI-powered data acquisition pipeline.

The current pipeline relies on a traditional threshold-based hardware trigger coupled with labor-intensive offline data cleaning that will become onerous for the next-generation LEGEND-1,000 experiment that will produce petabyte-scale datasets over the course of its lifetime.

LEAP-FWD directly addresses these challenges by developing and demonstrating AI algorithms deployable on field programmable gate arrays for real-time, low-latency event classification near the sensor edge.

Building upon recent semi-supervised machine learning (ML) approaches that combine unsupervised affinity propagation with support vector machines, this work will produce an advanced, hardware-optimized iteration for direct deployment on field programmable gate arrays towards operation on the current LEGEND-200 experiment and the future LEGEND-1000 iteration.

These capabilities will enable lower energy thresholds and automated event selection, laying the groundwork for an unsupervised spatiotemporal-aware anomaly detection algorithm that correlates signals across the entire detector array in real time, strengthening not only the discovery potential of rare physics signatures, but also the tagging of anomalous changes in the data quality of the experiment. 5.

AI-Driven Co-Design of Protein-Van der Waals Hybrids for High-Performance Microelectronics This project will develop an AI-driven co-design ecosystem to accelerate the discovery of emerging protein van der Waals (vdW) hybrids for energy-efficient non-von Neumann microelectronics.

The central vision is to leverage an AI-enabled scientific workflow that integrates molecular design, materials synthesis, high-throughput characterization, and device optimization to design protein-vdW-based devices with tunable chemistries and reversible, organized conductive filaments, thereby improving reproducibility, tunability, and energy efficiency.

Although vdW materials exhibit outstanding electronic properties that can be modulated by programmable protein structures and chemistry, their device functions arise from interrelated factors spanning multiple scales, including protein sequence and structure, assembly at vdW interfaces, reversible and controllable formation of conductive filaments, materials processing, and device-level optimization.

A major challenge is that conventional manual workflows treat these domains separately, leading to slow, fragmented, and inefficient discovery. The AI-based tools developed under this project will enhance the workflow by enabling closed-loop reasoning and optimization across molecular, materials, and device scales.

The proposed platform combines a tool-augmented large language model system with integrated deep-learning-based protein-inorganic interface design and a multi-agent AI platform for reasoning over experimental data and autonomous measurement workflows. The large language model will use physics-based protein simulations and structure-property reasoning to evaluate candidate protein vdW hybrids before synthesis.

The multi-agent AI will provide a structured interface between high-throughput experiments and modeling predictions by transmitting experimental observables and uncertainty estimates in one direction and predicted phase behavior and design recommendations in the other, thereby shortening design cycles and improving decision quality. 6.

Drift-Aware Initialization of Coupled Earth System Models for Scalable Sub-Seasonal-to-Seasonal Prediction Using Agentic AI Reliable prediction of water availability and hydrologic extremes at sub-seasonal-to-seasonal timescales is critical for U.S. energy systems, including hydropower operations, reservoir management, drought preparedness, and resilient energy-system planning.

However, coupled Earth system forecasts can lose skill after initialization because the atmosphere, land, ocean, and sea-ice components may not be dynamically balanced. These imbalances can produce initialization-induced drift, accelerate early forecast error growth, and degrade forecast trajectories for water-cycle processes.

Existing data assimilation, reanalysis-based initialization, and conventional drift correction reduce some errors, but they do not directly resolve state-dependent inconsistencies across the initialized coupled system. This leaves a gap between realized and potential forecast skill for water-for-energy applications.

This project will address this gap by developing a drift-aware, agentic AI framework within DOE’s Energy Exascale Earth System Model (E3SM) to improve the dynamical consistency of initialized coupled states. The framework will use a two-stage learn-then-correct strategy. First, a neural operator will learn lead-time and state-dependent drift as a structured response of the coupled system.

Second, a physics-constrained offline reinforcement learning method will generate bounded, physically consistent adjustments to selected initialization variables before forecast integration. Together, these components will enable an AI agent to assess the initialized coupled state, anticipate its likely drift, and select corrective actions under physical constraints.

This shifts AI from passive forecast post-processing toward active, constraint-aware initialization control. Multi-year initialized E3SM hindcasts will be employed to characterize drift across seasons and hydrologic regimes, validate the AI framework with independent test data, and deploy it in both standard E3SM and data-assimilation-enabled E3SM Atmosphere Model–Data Assimilation Research Testbed workflows.

Phase I performance will be compared with uncorrected forecasts and a conventional linear drift-correction baseline to quantify AI advantage. Evaluation will focus on reduced early forecast error growth during weeks 1–3, improved forecast skill during weeks 4–12, statistically significant gains in anomaly correlation coefficient, reduced forecast error, stable coupled-model behavior, and improved cost–skill efficiency.

Hydrologic evaluation will emphasize precipitation, snow water equivalent, soil moisture, runoff, total water storage anomaly, and river discharge in representative U.S. basins.

The project’s primary deliverable will be a validated, AI-enhanced E3SM initialization and hindcast workflow integrating the trained drift-prediction and correction framework with standard and E3SM Atmosphere Model–Data Assimilation Research Testbed-enabled coupled simulations.

By reducing initialization-induced drift, the project supports DOE Focus Area 15C and advances the capability from Technology Readiness Levels 3–4 toward a validated Technology Readiness Level 5 demonstration within E3SM. 7.

WASTECHAT: The First Tool Towards a Domain Foundational Model for DOE-EM The Department of Energy’s Office of Environmental Management (DOE-EM) is at a critical inflection point as decades of tacit subject matter expertise related to scientific measurements, operational records, and mission knowledge risk being lost.

Topic Area 6 called for scale-bridging AI systems that can integrate heterogeneous datasets, capture expert reasoning, and support high-consequence engineering workflows and the WasteCHAT tool will meet that call.

The Phase 1 development of WasteCHAT addresses this need by creating the first governed, machine-ready technical corpus for DOE-EM and deploying a domain-adaptive retrieval augmented generation system that uses scientific embedding models, metadata-rich chunking, and a grounding oriented large language model to provide citation anchored analysis of one of the most critical tank waste databases.

Phase 1 will transform two key datasets: first, the Hanford Best Basis Inventory and its associated technical records into normalized, provenance aware text enriched with temporal information, sampling lineage, tank identifiers, and most importantly metadata validated by subject matter experts, enabling WasteCHAT to answer multidecade chemistry and operations questions in minutes rather than days.

And second a machine-ready corpus from documented safety analysis reports, extending WasteCHAT to safety focused workflows that depend on tacit judgment from subject matter experts and deeply contextualized historical knowledge.

These foundations of WasteCHAT target further development in the support of training a fine-tuned DOE-EM domain foundation model capable of cross-site generalization, deep pattern recognition, and physics-informed reasoning that will broadly support AI implementation to accelerate DOE-EM cleanup missions; and towards establishing a functional AI platform locally at Hanford, which can form the basis of AI transformation of the cleanup missions .

This work establishes the wide data, governance, and platform infrastructure required for future domain model development and provides a scalable template for extending AI enabled performance improvements across the DOE complex. 8.

PORTUS: Automatic Performance Portability for Distributed Scientific Workflows on the Genesis Mission Platform The Genesis Mission will rise or fall on its ability to run agentic scientific workflows across the Genesis Mission Platform’s shifting mix of HPC and cloud resources. Today’s agentic workflows routinely change execution paths, synthesize new subgraphs online, and must cope with resources that contend, fail, and reconfigure.

Without a fundamental advance over today’s workflow orchestration, Genesis scientists will either hand-tune every port or accept order-of-magnitude performance losses. We propose to close this gap by coupling AI code generation with rigorous, compositional performance reasoning, a capability that industry has neither the incentive nor expertise to build.

Our central insight is that agentic workflows are assembled from a small vocabulary of recurring motifs.

If we can learn transferable surrogate models over these motifs, ground them in well-established memory and data-centric performance foundations, and introduce active learning on live telemetry, a coding agent then can reason about bottlenecks in a newly generated workflow fragment, rank alternatives, and retarget it to the current resource mix.

The payoff: an average domain scientist could describe a workflow in a few lines of natural language and then obtain a port that is far more concise (compared to hand-written) and faster than default schedules on data-intensive workloads. Our project will develop workflow performance models that guide coding agents with surrogate models that reason about workflow bottlenecks on the Genesis Mission Platform’s HPC and cloud resources.

Our project has three objectives. First, instead of memorizing whole workflows, our approach will learn transferable representations of workflow motifs by decomposing complex graphs into producer-consumer primitives.

Second, by combining our surrogate models with performance representations of distributed execution environments and architecture, we will rationally consider alternative subgraph realizations and alternative resource mappings. Third, the combination of dynamic workflow graphs, shifting resource availability means that dynamic feedback and adaptation is required, but that a static training set will not be sufficient. 9.

AI-Agentic Workflows to Advance Predictive Understanding of Fracture-Dominated Subsurface Energy Systems This Phase I project will develop and demonstrate an expert-in-the-loop, physics-informed AI-agentic workflow for Topic 17-C: Control of Subsurface Fractures.

The primary use case is understanding parent child well interference and fracture control during unconventional shale stimulation using curated Marcellus shale data (from the Marcellus Shale Energy and Environment Laboratory [MSEEL]) already available to our team. Our work will fuse MSEEL data to infer the evolving fracture connectivity and rank two key actionable control parameters: fracture-stage sequencing and pressure drawdown.

MSEEL data showed that parent wells can lose roughly 19% of their gas volume over 5 years due to interference from child wells. Using this dataset, we will test a defined fracture-control window in which operators compare a small set of actionable interventions that reduce well interference.

Even modest gains in interference control can preserve large gas volumes, improve recovery twofold, and translate into pad-scale economic value potentially in the millions of dollars. The scientific advance is a quantitative framework for determining which fracture-state variables are observable, predictable, and controllable from sparse, indirect, heterogeneous field data under uncertainty.

The core AI method is a verifiable multifidelity controller built on two AI components. First, physics-informed Fourier Neural Operators (FNOs) serve as surrogates for fracture-driven flow. FNOs learn operator mappings between function spaces, supporting transfer across mesh resolutions and parameterized fracture geometries with limited retraining.

Second, an agentic orchestration layer (e.g., using LangGraph, Grok Build) coordinates two specialized agents: 1) a fracture-aware data analysis agent and 2) a multiphysics modeling and forecasting agent.

These agents fuse multivariate field data, run FNO surrogates with calibrated uncertainty, and apply decision-conditioned routing to escalate only out‑of-distribution or high-uncertainty cases to coupled fracture flow, transport, and geomechanical simulations using PFLOTRAN.

The core AI method allows us to answer the scientific question: Can multivariate field data be assimilated into a reduced but decision-useful fracture state that is updated fast enough to support control decisions? The workflow is agentic because the controller autonomously invokes data, surrogate, uncertainty, and simulator tools under explicit uncertainty thresholds and expert-review guardrails.

The agent interface will be designed to support various simulators in Phase II. We also will assess the transferability on a multi-well geothermal site and deepwater use case to identify the modifications needed to define a Phase II fracture-control field validation.

The proposed work leverages NETL EDX Discover and DOE’s investments in the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. The multifidelity controller is designed so that other Genesis teams can adapt the fracture-control agent to their own subsurface science problems.

These outcomes will establish and evaluate a pathway for physics-grounded AI to improve predictive understanding and support fracture-control decisions while preserving expert oversight. The outputs will be released to the American Science Cloud (AmSC) and Transformational AI Models Consortium (ModCon) GitHub repository. 10.

AI-Driven Physics-Based Digital Twins for In Situ Uranium Mining Optimization This project will develop and demonstrate a prototype AI-enabled, physics-based digital twin for in situ recovery uranium mining to optimize wellfield pumping strategies, improve lixiviant sweep efficiency, and reduce excursion risk.

In situ recovery performance is strongly influenced by poorly constrained subsurface heterogeneity, including spatial variability in permeability, porosity, mineralogy, redox conditions, and hydraulic connectivity. These uncertainties affect fluid movement, uranium mobilization, recovery efficiency, and environmental performance, limiting operators' ability to predict outcomes and optimize operations with confidence.

The project will use site-specific data from Ur-Energy's Lost Creek in situ recovery operation, including well logs, pump-test-derived permeability estimates, and historical pumping records, to construct uncertainty-aware geologic realizations and define representative operating conditions.

A reduced-domain five-spot wellfield model will be simulated with the massively parallel PFLOTRAN reactive transport code to generate a baseline ensemble spanning plausible subsurface conditions. These simulations will provide both the reference case for performance evaluation and the training data for AI-enabled optimization.

This use of AI will provide the key performance advantage by enabling rapid exploration of operating strategies that would be impractical with full-physics simulations alone.

Specifically, the project will train a fast surrogate model on the PFLOTRAN simulation ensemble and embed it within a multi-objective evolutionary optimization framework to identify optimized pumping strategies that improve sweep efficiency while minimizing lixiviant losses associated with excursions.

The optimization will incorporate chance constraints to address geologic uncertainty, and an active-learning loop will selectively re-evaluate promising strategies with PFLOTRAN to improve surrogate accuracy in decision-relevant regions. 11.

EARL: Earth-Atmosphere Agentic Research and Learning—Physics-Constrained AI Closure Development for Cloud Microphysics and Turbulence This project will advance cloud microphysics and precipitation modeling on two fronts.

First, we will develop a physics-constrained ML closure for unresolved subgrid-scale (SGS) turbulence effects on cloud-droplet size distribution (DSD) broadening, formulated within two- and three-moment Predicted Particle Properties (P3) microphysics.

The closure will be trained using direct numerical simulation with super-droplet microphysics benchmarks for Pi Chamber-like conditions and matched P3 box-model simulations and then implemented in the Energy Research and Forecasting (ERF) model.

Second, co-developed with the closure, Earth-Atmosphere Agentic Research and Learning (EARL) will provide a human-supervised workflow for ERF closure development and agentic experimentation to explain why a given closure succeeds or fails.

Together, they will deliver a machine-learned representation of the turbulence–microphysics coupling that drives cloud-droplet growth toward rain and a workflow that is extensible across model components. The DOE water-for-energy mission depends on accurate forecasts of runoff, reservoir inflow, and cooling-water availability, all of which trace back to precipitation. Two barriers stand in the way.

First, existing bulk microphysics schemes omit SGS turbulence-driven DSD broadening that drives droplets to sizes at which collision-coalescence takes over. The result is delayed drizzle onset and errors in rainfall timing and amount. Second, closure development and model experimentation are serial and manual, limiting the range and rigor of candidate evaluation and the pace of growth in process-level understanding.

The project pursues two linked objectives, both of which demonstrate advantages of AI. Under AI-for-physics, we develop an ML closure for SGS supersaturation effects on cloud condensational growth in two- and three-moment P3, formulated as a learned residual correction to analytic grid-mean tendencies.

Candidate families include physics-constrained neural networks, symbolic regression, and hybrid parametric-plus-learned residual closures. The advantage of using AI is shown by improved DSD-broadening skill and stable ERF integrations.

Under AI-for-workflow, we build EARL as a portable, human-supervised agentic workflow that assembles data, generates and screens closures, and stages build–test–run–score cycles, and then expands it role to orchestrate model experimentation and hypothesis generation. The AI advantage is quantified by efficiency gains in closure development and experimentation.

EARL’s agentic experimentation converts faster closure development into greater process-level understanding, delivering an advantage that neither front achieves alone. The primary deliverable is a validated SGS condensational-growth closure deployed in the project’s P3 implementation in ERF, reducing DSD-broadening errors that propagate into water for-energy predictions.

Because P3 is also the microphysics scheme in SCREAM and other E3SM configurations, the closure offers a direct transfer path into DOE’s global model ecosystem. EARL extends the project’s reach further as a portable, human-supervised agentic workflow reusable across ERF model development and experimentation, positioned for Genesis Mission coordination, American Science Cloud readiness, and broader reuse. 12.

AI-Guided Model-Experiment Framework for Predicting Water Availability in Energy Systems Using Multi-Fidelity Watershed Modeling Water availability in watersheds is governed by coupled interactions among atmospheric forcing, land-surface processes, and subsurface hydrology across multiple spatial and temporal scales.

Predicting these interactions remains a fundamental challenge because nonlinear feedback among precipitation, evapotranspiration, groundwater storage, and stream–aquifer exchange combine to introduce significant uncertainty that is difficult to quantify and interpret. This uncertainty directly impacts energy-relevant decisions related to hydropower generation, thermoelectric cooling, reservoir operations, and drought resilience.

This effort proposes an AI-guided Model–Experiment framework that integrates data, mechanistic modeling, and structured reasoning to improve prediction and reduce uncertainty.

The framework uses the Advanced Terrestrial Simulator as the mechanistic prediction engine, SciLink as the semantic and executable layer for data integration and model orchestration, and the Agentic Discovery and Exploration Platform for Tools as the reasoning layer that translates water–energy questions into model-informed hypotheses, guides informative simulations and observational comparisons, and interprets outcomes under uncertainty and ambiguity.

Because the Advanced Terrestrial Simulator is computationally expensive at the ensemble sizes and spatial resolutions needed for uncertainty-aware prediction, the team will also use multifidelity and surrogate models to enable rapid scenario exploration while preserving physical realism.

During Phase I, we will build and test this framework to predict late-summer streamflow and groundwater heads relevant to municipal supply reliability and prospective cooling-water demand in the South Branch Kishwaukee Watershed (Illinois), with the Oak Creek Watershed (Washington) serving as the technical benchmark and validation watershed.

Using an agentic workflow that links data ingestion, simulation, evaluation, reasoning, and human validation in a closed loop, the team plans to quantify the dominant drivers and uncertainties of seasonal water availability, including the roles of forcing, parameters, and model structure. The effort will demonstrate measurable gains in predictive skill, uncertainty attribution, and workflow efficiency relative to a non-agentic baseline.

This project will provide a validated prototype demonstrating the advantage of using AI through improved predictive skill, stronger uncertainty attribution, and faster model–experimental iterations. By tightly coupling execution, mechanistic modeling, and reasoning in a closed-loop framework, this project will advance uncertainty-aware, decision-relevant prediction of water availability for energy systems.

This capability can support better planning for drought, cooling-water reliability, and flood-related infrastructure risk. More broadly, the project will lay the groundwork for scalable AI‑enabled water prediction tools with regional and national relevance. 13.

Autonomous Quantum Amplifier Workflow Optimization and Learning Framework (AQUA-WOLF) Quantum sensing, quantum information science, and HEP dark matter searches depend critically on near-quantum-limited microwave amplifiers that must be precisely tuned to operate at peak performance.

Finding the right operating point for these devices requires slow, manual parameter searches that consume significant operator time and limit experimental throughput. As these experiments grow in scale and complexity, this bottleneck threatens to become a fundamental limit on scientific productivity.

The Autonomous Quantum Amplifier Workflow Optimization and Learning Framework replaces manual tuning with an autonomous AI agent that learns to operate quantum parametric amplifiers without human intervention. The agent controls the key tuning parameters and uses measured device performance as feedback,