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RESEARCH PAPER · Unknown journal · 2020

Integrating Physics-Based Modeling with Machine Learning: A Survey

Jared Willard, Xiaowei Jia, Shaoming Xu, Michael S. Steinbach, Vipin Kumar

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Integrating Physics-Based Modeling with Machine Learning: A SurveyOriginalDeep Learning Based Integrators for Solving Newton's Equations with Large Timesteps5การผสมความรทางวทยาศาสตรกอนหนาเขากบการเรยนรเชงลกสำหรบพยากรณปรมาณฝนในระยะสนจากภาพถายเรดาร0Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles40Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications3Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics.1Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications109Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting8Application of generative deep learning to predict temperature, flow and species distributions using simulation data of a methane combustor38Deep neural networks for nonlinear model order reduction of unsteady flows223Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks22Spacetime Autoencoders Using Local Causal States3Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation14A Brief Review of Popular Machine Learning Algorithms in Geosciences1A linear hybrid model for enhanced servo error pre-compensation of feed drives with unmodeled nonlinear dynamics20EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology10Hybrid Modelling of Environmental Processes using Composite Models5Machine-Learning Digital Twin of Overlay Metal Deposition for Distortion Control of Panel Structures6Non-intrusive reduced order modelling for aerodynamic applications1On closures for reduced order models— A spectrum of first-principle to machine-learned avenues122Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks101Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs47Why Can’t Neural Networks Forecast Pandemics Better1Digital twin, physics-based model, and machine learning applied to damage detection in structures367Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning86Physics-informed machine learning: case studies for weather and climate modelling662A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables25Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders3Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling9Land Cover Mapping in Limited Labels Scenario: A Survey2Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems441Data-driven simulation in fluids animation: A survey12Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series “Clouds and Climate”)37Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data51Towards Error Measures which Influence a Learners Inductive Bias to the Ground Truth3Does statistical model perform at par with computationally expensive general circulation model for decadal prediction?6Injecting knowledge in data-driven vehicle trajectory predictors72Automated Knowledge Discovery using Neural Networks1Integration of neural networks with numerical solution of PDEs for closure models development15Performance Comparison for Scientific Computations on the Edge via Relative Performance5CNNs and GANs in MRI-based cross-modality medical image estimation4Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression20Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers0The rise of data-driven modelling17Incorporation of Physics into Machine Learning for Production Prediction from Unconventional Reservoirs: A Brief Review of the Gray-Box Approach45Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling0Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning87CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 16Hybrid Approach for Digital Twins in the Built Environment12Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control35Incorporating Prior Scientific Knowledge Into Deep Learning for Precipitation Nowcasting on Radar Images6Influence of the choice of stream temperature model on the projections of water temperature in rivers52Simulated Data Generation through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling0Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations28Topology Generation for Ion-thruster Grids3Simulation of multi-species flow and heat transfer using physics-informed neural networks200Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing136A machine learning approach for maximizing direct current power of nonlinear energy harvesting systems subjected to periodic impulse excitation5Physical Equation Discovery Using Physics-Consistent Neural Network (PCNN) Under Incomplete Observability16PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics36Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights594A Hybrid Filtered Basis Functions Approach for Tracking Control of Linear Systems with Unmodeled Nonlinear Dynamics1Structure and properties of alkali aluminosilicate glasses and melts: Insights from deep learning54Perspective: Machine Learning of Thermophysical Properties60Identifying the most suitable machine learning approach for a road digital twin25On closures for reduced order models—A spectrum of first-principle to machine-learned avenues150Characterizing possible failure modes in physics-informed neural networks116Using physics to extend the range of machine learning models for an aerodynamic, hydraulic and combusting system: The toy model concept14Composing Modeling and Simulation with Machine Learning in Julia5Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing20Modia and Julia for Grey Box Modeling6NeuralFMU: Towards Structural Integration of FMUs into Neural Networks9Critical role of DNA unwinding and Cas9 conformational changes in modelling off-target effects0Paradigm Shift Through the Integration of Physical Methodology and Data Science0Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation2Physics-Informed Machine Learning for Structural Health Monitoring62Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis7Hybrid modeling of the human cardiovascular system using NeuralFMUs7OpenFWI: Benchmark Seismic Datasets for Machine Learning-Based Full Waveform Inversion.2Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis1Physics-enhanced deep surrogates for PDEs.0Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation0Domain-Guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation3A hybrid partitioned deep learning methodology for moving interface and fluid–structure interaction48Application of a mixed variable physics-informed neural network to solve the incompressible steady-state and transient mass, momentum, and energy conservation equations for flow over in-line heated tubes81Bibliography0Polyconvex anisotropic hyperelasticity with neural networks236Deep neural network enabled corrective source term approach to hybrid analysis and modeling29On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion2Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems19Tempered fractional LES modeling20Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering3Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks8Prediction of Ultrasonic Guided Wave Propagation in Fluid–Structure and Their Interface under Uncertainty Using Machine Learning5Digital Twin for automated guided vehicles fleet management34Fusing physics-based and deep learning models for prognostics369Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps19Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation19Modeling of water waves and sediment transport using physics-based and machine learning methods0When is gray-box modeling advantageous for virtual flow metering?1A neural network super-resolution approach for thereconstruction of coastal sea states0An intelligent system for reflow oven temperature settings based on hybrid physics-machine learning model34Atikokan Digital Twin: Machine learning in a biomass energy system61Physics-Informed LSTM hyperparameters selection for gearbox fault detection224Tackling Climate Change with Machine Learning1015Digital Transformation - Towards New Frontiers and Business Opportunities8Enhancing the Forecast of Ocean Physical Variables through Physics Informed Machine Learning in the Santos Estuary, Brazil1Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data8A Hybrid Physics-Based and Machine-Learning Approach for Stick/Slip Prediction12APIK: Active Physics-Informed Kriging Model with Partial Differential Equations21A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics34Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future32A geologically-constrained deep learning algorithm for recognizing geochemical anomalies56A transfer learning-based digital twin for detecting localised torsional friction in deviated wells33NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)19Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study90Physics-aware nonparametric regression models for Earth data analysis15Towards damage detection using physics-based modeling and data-driven optimization1From CNNs to GANs for cross-modality medical image estimation50CoPhy -PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems42Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations16On Recurrent Neural Networks for learning-based control: Recent results and ideas for future developments91Data-driven modeling of wind waves in upper Delaware Bay with living shorelines17Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models32Perspectives on the integration between first-principles and data-driven modeling225Data-driven physics-informed interpolation evolution combining historical-predicted knowledge for remaining oil distribution prediction23Reconstruction of nearshore wave fields based on physics-informed neural networks66Latent Space Simulation for Carbon Capture Design Optimization3Reinforcement learning and approximate Bayesian computation for model selection and parameter calibration applied to a nonlinear dynamical system16Learning Snow Layer Thickness Through Physics Defined Labels8Composing Modeling And Simulation With Machine Learning In Julia24Physics guided neural networks for modelling of non-linear dynamics92Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging9Requirements Engineering for Machine Learning: A Review and Reflection38Predictions on multi-class terminal ballistics datasets using conditional Generative Adversarial Networks14Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning49A neural-corrector method for prediction of the vertical motions of a high-speed craft14Machine learning for understanding inland water quantity, quality, and ecology5Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term Memory12A physics-constrained neural network for multiphase flows24A Perspective on Data-Driven Coarse Grid Modeling for System-Level Thermal Hydraulics7Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-Based Approaches180Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems14Dynamic Car-following Model Calibration with Deep Reinforcement Learning8Machine learning applications in river research: Trends, opportunities and challenges64Pairing Digital Twins with Maritime Autonomy2Super-resolving 2D stress tensor field conserving equilibrium constraints using physics-informed U-Net13DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects34Physics-Informed Graph Learning8Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges372Structural Deterioration Knowledge Ontology towards Physics-Informed Machine Learning for Enhanced Bridge Deterioration Prediction29

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  1. Deep Learning Based Integrators for Solving Newton's Equations with Large Timesteps

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  2. การผสมความรทางวทยาศาสตรกอนหนาเขากบการเรยนรเชงลกสำหรบพยากรณปรมาณฝนในระยะสนจากภาพถายเรดาร

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  3. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

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  4. Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications

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  5. Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics.

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  7. Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

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  8. Application of generative deep learning to predict temperature, flow and species distributions using simulation data of a methane combustor

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  11. Spacetime Autoencoders Using Local Causal States

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  12. Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation

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  19. On closures for reduced order models— A spectrum of first-principle to machine-learned avenues

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  20. Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks

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  27. Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders

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  38. Integration of neural networks with numerical solution of PDEs for closure models development

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  39. Performance Comparison for Scientific Computations on the Edge via Relative Performance

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  41. Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

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  42. Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers

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  43. The rise of data-driven modelling

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  44. Incorporation of Physics into Machine Learning for Production Prediction from Unconventional Reservoirs: A Brief Review of the Gray-Box Approach

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  45. Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

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  46. Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning

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  47. CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

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  48. Hybrid Approach for Digital Twins in the Built Environment

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  49. Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

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  50. Incorporating Prior Scientific Knowledge Into Deep Learning for Precipitation Nowcasting on Radar Images

    International Joint Conference on Computer Science and Software Engineering · 2021-06-30

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