Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
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Nguyen, Kyle Cuongthe [Sandia National Laboratorie↗
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SysCaps (Language Interfaces for Simulation Surrogates of Complex Systems) [SWR-24-97]
You've found the official code repository for the paper "SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems," presented at the Foundation Models for Science: Progress, Opportunities, and Challenges workshop at NeurIPS 2024. Our paper conjectures that interfaces (both text templates as well as conversational) makes interacting with simulation surrogate models for complex systems more intuitive and accessible for both non-experts and experts. "System captions", or SysCaps, are text-based descriptions of systems based on information contained in simulation metadata. Our paper's goal is to train multimodal regression models that take text inputs (SysCaps) and timeseries inputs (exogenous system conditions such as hourly weather) and regress timeseries simulation outputs (e.g. hourly building energy consumption). The experiments in our paper with building and wind farm simulators, which can be reproduced using this codebase, aim to help us understand whether a) accurate regression in this setting is possible and b) if so, how well can we do it. Paper: https://arxiv.org/abs/2405.19653
Emami, Patrick↗
Advanced Ceramic Membranes/Modules for Ultra Efficient Hydrogen (H2) Production/Carbon Dioxide (CO2) Capture for Coal-Based Polygeneration Plants: Fabrication, Testing, and CFD Modeling
Inorganic membrane-based systems are a promising technology for precombustion CO2 capture with simultaneous H2 production. State-of-the-art packages for high temperature and pressure service consist of multiple tube membrane bundles prepared in a "candle filter" configuration, in which the membrane tubes are open at one end and sealed at the other. This configuration is used for practical reasons, specifically the need to minimize problems due to thermal expansion mismatch between the ceramic tube bundle and the steel housing. However, the primary technical problem with the candle filter format for commercial-scale installations is the inability to purge the tube side (typically the permeate side), a feature that is crucial for high H2 recovery. In this study, the focus is to design and fabricate the first dual-end open full ceramic multiple tube membrane bundle that enables tube side (permeate) purge for gas separation applications. An additional key feature of this design is the simplified module layout, as the membrane bundles can be installed end-to-end with tube side (permeate) flow directly from one bundle to the next. This layout simplifies the membrane to housing seals and yields significant improvement in membrane packing density. Detailed focus areas in our studies include: (i) Materials development and preparation of the tube-to-tube sheet potting for the dual-ended bundle; (ii) the sealing and optimal module configuration design to minimize membrane stress upon module mounting; (iii) the demonstration, via the fabrication of CMS and Pd-alloy membranes supported on full-size, dual-ended ceramic support bundles, of the first example of a purgeable ceramic membrane and module; and (iv) development of a CFD model of the membrane module for calculation of feed flow distribution, and for use in scale-up, and capital cost estimating. The CFD model was validated using experimental data with the multi-tubular membrane system, employing He/N2 as a model gas mixture (surrogate for H2/CO2), and has been shown to be quite accurate. Employing the model, we are able to study the effects of operating pressure and temperature, feed and sweep gas flow rates, and the choice of membrane tube configuration on system performance.
20 FOSSIL-FUELED POWER PLANTS↗
Advanced Ceramic Membranes/Modules for Ultra Efficient Hydrogen (H2) Production/Carbon Dioxide (CO2) Capture for Coal-Based Polygeneration Plants: Fabrication, Testing and CFD Modeling
Inorganic membrane-based systems are a promising technology for precombustion CO2 capture with simultaneous H2 production. State-of-the-art packages for high temperature and pressure service consist of multiple tube membrane bundles prepared in a "candle filter" configuration, in which the membrane tubes are open at one end and sealed at the other. This configuration is used for practical reasons, specifically the need to minimize problems due to thermal expansion mismatch between the ceramic tube bundle and the steel housing. However, the primary technical problem with the candle filter format for commercial-scale installations is the inability to purge the tube side (typically the permeate side), a feature that is crucial for high H2 recovery. In this study, the focus is to design and fabricate the first dual-end open full ceramic multiple tube membrane bundle that enables tube side (permeate) purge for gas separation applications. An additional key feature of this design is the simplified module layout, as the membrane bundles can be installed end-to-end with tube side (permeate) flow directly from one bundle to the next. This layout simplifies the membrane to housing seals and yields significant improvement in membrane packing density. Detailed focus areas in our studies include: (i) Materials development and preparation of the tube-to-tube sheet potting for the dual-ended bundle; (ii) the sealing and optimal module configuration design to minimize membrane stress upon module mounting; (iii) the demonstration, via the fabrication of CMS and Pd-alloy membranes supported on full-size, dual-ended ceramic support bundles, of the first example of a purgeable ceramic membrane and module; and (iv) development of a CFD model of the membrane module for calculation of feed flow distribution, and for use in scale-up, and capital cost estimating. The CFD model was validated using experimental data with the multi-tubular membrane system, employing He/N2 as a model gas mixture (surrogate for H2/CO2), and has been shown to be quite accurate. Employing the model, we are able to study the effects of operating pressure and temperature, feed and sweep gas flow rates, and the choice of membrane tube configuration on system performance.
20 FOSSIL-FUELED POWER PLANTS↗
Surrogate Constructed Scalable Circuits ADAPT-VQE in the Schwinger model
Inspired by recent advancements of simulating periodic systems on quantum computers, we develop a new approach, (SC)$^2$-ADAPT-VQE, to further advance the simulation of these systems. Our approach extends the scalable circuits ADAPT-VQE framework, which builds an ansatz from a pool of coordinate-invariant operators defined for arbitrarily large, though not arbitrarily small, volumes. Our method uses a classically tractable ``Surrogate Constructed'' method to remove irrelevant operators from the pool, reducing the minimum size for which the scalable circuits are defined. Bringing together the scalable circuits and the surrogate constructed approaches forms the core of the (SC)$^2$ methodology. Our approach allows for a wider set of classical computations, on small volumes, which can be used for a more robust extrapolation protocol. While developed in the context of lattice models, the surrogate construction portion is applicable to a wide variety of problems where information about the relative importance of operators in the pool is available. As an example, we use it to compute properties of the Schwinger model - quantum electrodynamics for a single, massive fermion in $1+1$ dimensions - and show that our method can be used to accurately extrapolate to the continuum limit.
Gustafson, Erik [RIACS, Mtn. View] (ORCID:00000001↗
Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints
We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.
42 ENGINEERING↗
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.
97 MATHEMATICS AND COMPUTING↗
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization
We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete.
adaptation models↗
Modeling of Radiolytic Hydrogen Generation of Irradiated Surrogate Aluminum Plates
The hydrogen generation from oxyhydroxide layers under irradiation is an important effect to understand for the long-term storage of aluminum-clad spent nuclear fuel. The original chemical model shows steady production of hydrogen from aluminum oxyhydroxide layers, with no roll-over effect in the curves of the hydrogen concentration until significant pressure has built up within the canister. This study uses a variety of kinetic fits to adjust the old chemical model to have roll-over effects that are consistent with new high-dose experiments. The data from aluminum 6061 coupon tests at high doses were used to fit several kinetic models. The 6061 aluminum samples are representative of the aluminum-clad fuel from the ATR. The fitted kinetic models were then used in a model of a mini-canister with much higher packing density to determine which kinetic rates would fit both sets of experimental tests. The rate constants for the kinetic mechanisms that were close were then tweaked such that the chemical models fit both the experimental data from the capsule tests and the data from the mini-canister tests. The kinetic models which fit the best are either inhibition by hydrogen, or a back reaction wherein H radical is reabsorbed onto the oxyhydroxide layer. The H back reaction rolls over quicker and may fit future collected data.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Scale-bridging Optimization Framework for Desalination Integrated Produced Water Networks
Produced water (PW) management poses a major challenge to U.S. oil and gas development. Due to freshwater scarcity and PW injection curtailments, desalination may become a necessity in some parts of the country. Integrating rigorous desalination process models within a produced water network for co-optimization of desalination design and network operation can be challenging to scale-up due to computational complexity. In this work we use the trust region filter framework to decompose the integrated optimization problem into an optimization master problem consisting of the network model and simplified surrogates for the desalination units and an optimization sub problem for the rigorous desalination unit model. The approach is demonstrated on a multiperiod produced water network from the PARETO library with thermal desalination units.
Naik, Sakshi↗
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids
In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.
36 MATERIALS SCIENCE↗
Autoignition of CRC diesel surrogates at low temperature combustion conditions: Rapid compression machine experiments and modeling
As federal programs require increasingly stringent engine emissions and fuel economy standards, these ambitions can only be met if next-generation combustion technology is developed focusing on high-efficiency and low-emissions engines. Recent research has indicated the need to operate engines at higher compression ratios and with low temperature combustion (LTC) to achieve the needed gains in engine efficiency and reductions in emissions. Because there is a lack of understanding of the chemistry of diesel fuel components and their mixtures at these LTC conditions, this limits the ability to develop predictive chemical kinetic models that can be used to optimize engine combustion. The current study aims to fill in gaps in fundamental combustion data on surrogate fuel mixtures relevant to diesel fuels. Specifically, four multicomponent diesel surrogates formulated by the Coordinating Research Council (CRC) to emulate an ultra-low-sulfur research-grade #2 certification diesel fuel (CFA), namely V0a (4 components), V0b (5 components), V1 (8 components), and V2 (9 components), have been investigated in a rapid compression machine (RCM) through determination of total and first-stage ignition delay times. Autoignition characteristics of lean to rich fuel/O 2 /N 2 mixtures, for the four CRC surrogates and CFA, have been measured using an RCM at LTC relevant pressures and temperatures, in the ranges of 10–20 bar and 650–1000 K, respectively. The equivalence ratios have been varied by independently changing the oxygen mole fraction and the fuel mole fraction in the test mixtures, thereby illustrating the individual effects of oxygen concentration and fuel loading on diesel autoignition. Autoignition results of these four CRC surrogates are compared among them and with those of CFA. Some degree of agreement in autoignition response between each CRC surrogate and CFA is observed, while discrepancies are also identified and discussed. In addition, a detailed chemical kinetic model for diesel surrogates has been developed and validated against these newly-acquired RCM data. We find that this model shows reasonable agreement with the overall ignition delay time results of the current RCM experiments. Chemical kinetic analyses of the developed model were further conducted to help identify the reactions controlling the autoignition processes and the consumption of fuel components in CRC surrogates.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Neural-Network-Enhanced COTSIM: Advancing Predictive Capabilities for Fast DIII-D Simulations
Sustaining fusion reactions in tokamaks requires heating plasma to thermonuclear temperatures while maintaining confinement and stability. Neutral beam injection (NBI) provides heating, current drive, torque, and fueling, while electron cyclotron (EC) waves are widely used for heating and current drive; together, these actuators shape the plasma current, temperature, and density profiles. The control-oriented tokamak simulator (COTSIM), a predictive, control-oriented code, has been enhanced with neural-network surrogates for transport and sources. Turbulent transport is predicted by MMMnet—a neural-network version of the updated multimode model (MMM 9.0.10)—with significantly reduced computation time relative to MMM; neoclassical transport follows the Chang–Hinton model. NUBEAMnet, a surrogate of the Monte Carlo NUBEAM module, predicts beam-driven heating, current, and torque. EC heating and current drive use a control-oriented, empirically scaled source model; plasma resistivity follows the Spitzer formulation; bootstrap current uses the Sauter model. Equilibrium is computed using both prescribed and fixed-boundary solvers (FBSs), and the pedestal structure is modeled with an empirical pedestal model. For a representative DIII-D discharge, COTSIM predicts electron and ion temperature and safety-factor profiles in close agreement with TRANSP predictive and interpretive simulations while extending predictions through the pedestal region to the plasma edge (versus 80% of the minor radius in TRANSP). Furthermore, the equivalent COTSIM simulation runs in under 3 min compared to about 2 h for TRANSP, enabling rapid scenario planning, optimization of tokamak operation, and between-pulse control design.
Control-oriented tokamak simulator (COTSIM)↗
New insights into fuel blending effects: Intermolecular chemical kinetic interactions affecting autoignition times and intermediate-temperature heat release
Fuel blending effects on chemically-dominated fuel properties, such as gasoline anti-knock quality, are influenced by fundamental chemical kinetic interactions between the blending agent and the base fuel. Historically, quantification of such interactions has focused on changes to the radical pool, including $\dot{O}$H and HO 2 , while intermolecular interactions pertaining to carbonated, non-fuel-specific intermediates are typically overlooked. In this regard, this work aims to derive new insight into intermolecular chemical kinetic interactions that are intrinsic to fuel blending effects via a case study on blends of 0–30% ethanol (by volume) into FGF-LLNL (a multi-component gasoline surrogate for FACE-F research gasoline) using a rapid compression machine at a diluted/stoichiometric fuel loading, compressed pressure of 40 bar and low- to intermediate-temperature regimes that are representative of boosted SI engine operation. Ethanol blending effects on the intermediate temperature heat release (ITHR) of FGF-LLNL are characterized using experimental measurements, where ethanol is found to promote the extent of ITHR and suppress the transition from ITHR to main ignition. Chemical kinetic modeling is undertaken using a recently updated gasoline surrogate model. Sensitivity analyses on ITHR characteristics further corroborate the ethanol blending effects, and highlight the significant dependence of ITHR on both fuel-specific and non-fuel-specific reactions. An approach allowing comprehensive characterization of the complex intermolecular chemical kinetic interactions between constitutes in a fuel blend is then proposed. Application of the approach to FGF-LLNL/E0–E30 reveals that ethanol perturbs the heat release and autoignition characteristics of FGF-LLNL not only by directly changing the $\dot{O}$H and HO 2 radical pools via fuel-specific reactions, but also through intermolecular interactions where participating intermediates can be produced and consumed by various sub-chemistries. Disabling the intermolecular interactions in carbonated species between ethanol and FGF-LLNL sub-chemistries leads to somewhat slower ITHR evolution and lower ignition reactivity. The role of the individual intermolecular interaction is also characterized using the proposed approach. Lastly, implications of intermolecular interactions for future studies that aim to improve model performance are highlighted, where it is found that further investigations on the core C0-C4 chemistry are needed for developing highly accurate chemistry models for complex fuel blends.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
IDAES-PSE 2.7.0 Release
The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS
AS↗
Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate
The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.
Neural ordinary differential equations↗
Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates
We report surrogate mixtures are routinely used for understanding gasoline fuel combustion in engine simulations. The general trend in surrogate formulation has been to increase the number of fuel components in a mixture to better emulate real fuel properties. Recently, a new surrogate design strategy based on functional group analysis of real gasolines was proposed using a minimal number of species [minimalist functional group (MFG)-approach]. MFG surrogates (having just one or two components) could experimentally capture the ignition delay time (IDT), threshold sooting index, and smoke point of different gasoline fuels with hundreds of components. However, other combustion characteristics were not explored, and kinetic modeling of MFG surrogates was not reported. These aspects are addressed in this paper, where the combustion behavior of MFG surrogates for various gasolines was assessed by simulating IDT, jet-stirred reactor oxidation, and premixed laminar flame speeds using chemical kinetic modeling. MFG simulations were compared with experimental data of the real gasolines as well as with the more complex multicomponent (five to nine species) surrogates. This study reveals that binary MFG surrogate mixtures are capable of accurately simulating the combustion behavior of more complex gasoline fuels with hundreds of components.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar
Model parameters are a major source of uncertainty in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the most sensitive parameters has the potential to improve solar irradiance predictions. Here, we adopted a multiobjective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California, in order to focus on parameter calibration under cloudy conditions with different aerosol loading. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE. Our investigation reveals, however, that optimizing TE14 has a limited impact on irradiance simulations under less-polluted conditions in the SGP.
14 SOLAR ENERGY↗