Paper tackles running physics model training, improving reinforcement learning efficiency.
problem Training a physiologically-based human model to navigate complex obstacle courses efficiently.
method Physics-based reinforcement learning, using Deep Deterministic Policy Gradient, with improvements like layer normalization and parameter noise.
result Deep Deterministic Policy Gradient proved most efficient, and improvements helped stabilize training.
Google Research Football: A new 3D physics-based game for reinforcement learning.
problem Training reinforcement learning algorithms in complex, realistic environments.
method Developed a new 3D physics-based football simulator environment.
result Reported baseline results for various reinforcement algorithms.
PFPN uses particle filtering to improve character control in physics-based simulations.
problem Premature commitment to suboptimal actions in high-dimensional continuous control problems for articulated characters.
method Proposes a particle-based action policy using particle filtering to dynamically explore and discretize the action space.
result Demonstrates better imitation performance and robustness to external perturbations compared to Gaussian policies.
Catalyst.RL accelerates RL research with efficient training.
problem Efficient reinforcement learning training in complex environments.
method Open-source PyTorch framework with distributed training and RL algorithms.
result Catalyst.RL achieved 2nd place in a computationally expensive RL challenge.
PPOPT uses pretraining to speed up reinforcement learning in physics simulations.
problem High computational costs and inefficiency in reinforcement learning with small training samples.
method A novel policy neural network architecture that combines pretraining and fully-connected networks.
result PPOPT outperforms classic PPO on small training samples in terms of rewards and stability.
A robot learns environmental fields using physics-based models and Bayesian methods.
problem Accurately learning complex environmental fields from limited robot measurements.
method Bayesian framework with Gaussian processes to select and update physics-based models in real-time.
result The robot's learned flow field approximates real flow better than prior solutions and data-driven methods.
CARL controls a quadruped to move naturally in complex environments.
problem Motion synthesis in dynamic environments with complex constraints.
method CARL uses GANs to adapt high-level controls to action distributions and deep reinforcement learning for dynamic recovery.
result CARL can be controlled with high-level directives and react naturally to dynamic environments.
PGNN combines physics models with neural nets for lake temperature prediction.
problem Lake temperature modeling with physical constraints.
method Physics-guided neural networks using hybrid modeling and physics-based loss functions.
result PGNN improves generalizability and scientific consistency in lake temperature predictions.
Deep learning speeds up oil reservoir simulations by 2000x.
problem Accelerating oil reservoir simulations using physics-based methods.
method Developed a neural network proxy model for oil reservoirs.
result Achieved a speedup of more than 2000X with an average sequence error of 10%.
Researchers use GANs to infer physics-based inverse problems, quantifying uncertainty and promoting generalizability.
problem Quantifying uncertainty in physics-based inverse problems.
method Trained conditional Wasserstein GANs with U-Net architecture and conditional instance normalization.
result The approach effectively samples from the posterior and promotes generalizability with out-of-distribution samples.
Combines physics-based ML with hierarchical Bayesian techniques for better model performance.
problem Lack of physical knowledge in black-box machine learning models.
method Embeds physics-based models into Gaussian Process mean function and uses kernel machines to characterize discrepancies.
result Improved model performance under blind conditions through integration of physics-based knowledge.
Bayesian hybrid models correct for missing physics in machine learning.
problem Systematic bias in machine learning models.
method Fusing physics-based insights with machine learning constructs, using Bayesian calibration and stochastic programming.
result Bayesian hybrid models outperform pure machine learning approaches with less data.
GINNs combine deep learning with PGMs for physics-based multiscale systems.
problem Intrinsic computational bottlenecks and lack of sufficient data for QoI estimation.
method Hybrid approach combining deep learning with probabilistic graphical models, informed by structured priors for CVs.
result GINNs produce tight confidence intervals for non-Gaussian QoIs.
DynNet models dynamic responses of linear and nonlinear systems with fewer variables and higher accuracy.
problem Predicting dynamic responses of linear and nonlinear systems.
method Physics-based recurrent neural network with optimized architecture and training techniques.
result Higher accuracy and fewer trainable variables compared to existing models.
Physics-based framework improves building energy forecasting.
problem Lack of physical correspondence in machine learning models for building energy systems.
method Combines LTI SSMs with subspace-based domain adaptation (SDA).
result Physics-derived subspaces align with data-derived subspaces for better forecasting.
Physics-based deep learning improves fiber-optic communication efficiency.
problem Improving signal propagation in fiber-optic communication systems.
method Parameterizing the split-step method of solving the nonlinear Schrödinger equation as a deep neural network.
result Filters can be pruned to as few as 3 taps/step without sacrificing performance.
PhysicsNAS generalizes PBL by integrating NAS into neural networks with physical priors.
problem Leveraging physical priors for robust inference in machine learning.
method Physics-based neural architecture search (PhysicsNAS) integrating physical priors and neural networks.
result PhysicsNAS is a top-performer across various physical models and dataset qualities.
We enhance autonomous materials research with problem-aware models.
problem Complex decision-making in autonomous materials.
method Bayesian framework, machine learning, physics-based models, operational considerations.
result Improved models reflect problem-specific structure.
Physics-based method approximates mean curvature on surface meshes.
problem Estimating mean curvature on triangulated surfaces.
method Derives approximation from Young-Laplace equation and force balance.
result Approximation equivalent to discrete Laplace-Beltrami operator.
Neural network residuals isolate and locate unknown faults.
problem Locating unknown faults in industrial systems.
method Neural network-based residuals combining physical insights and machine learning.
result Neural network residuals can isolate and locate unknown faults.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
Paper improves satellite collision prediction using ML.
problem Current orbit predictions fail due to lack of space data.
method Integrates physics-based models with ML for improved accuracy.
result ML approach reduces prediction errors and improves generalization.
Paper integrates ML with physics models for engineering and environmental challenges.
problem Complex science and engineering problems require new methodologies combining physics-based models and ML.
method Structured overview of integrating physics-based models with ML techniques.
result Taxonomy of existing techniques and potential research gaps identified.
New model learns better policies from expert demonstrations with higher efficiency.
problem Learning accurate policies from expert demonstrations with high efficiency.
method Generative adversarial imitation learning (GAIL) model that learns f-divergence automatically. result Learns better policies with higher data efficiency in physics-based control tasks.
Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior probability of the enti…
Proposes a physics-informed VAE for disentangling physics from confounding influences.
problem Challenges in inferring and predicting physical systems under partial knowledge.
method Physics-informed variational autoencoder with adversarial training.
result Model successfully disentangles known physics from confounding influences.
PIML model improves hydrological predictions by blending physics and ML.
problem Hydrological models either lack predictive accuracy or fail to maintain physical consistency.
method Physics Informed Machine Learning (PIML) that integrates physics-based models and ML algorithms.
result PIML model outperforms both physics-based and ML models in predicting streamflow and evapotranspiration.
A deep learning approach solves probabilistic inverse problems with physical constraints.
problem Solving inverse problems with large inferred vectors and prior samples.
method Uses conditional Wasserstein generative adversarial networks (cWGAN) with full gradient penalty.
result Improves accuracy and robustness in sampling and solving inverse problems.
A physics-based method improves data interpolators and regression tasks.
problem Improving accuracy and efficiency in function learning.
method Inspired by statistical mechanics, introduces corrections to minimize energy.
result Improves performance in interpolation and regression tasks, especially in high-dimensional spaces.
Physics-guided models improve lake temperature and quality predictions.
problem Predicting and monitoring water temperature and quality in lakes.
method Combining physics-based models and recurrent neural networks with physical constraints.
result Improved prediction accuracy and scientific consistency.
Semi-parametric framework for nonlinear system identification
problem Nonlinear system identification
method Orthogonal Gaussian process regression
result Interpretable models from incomplete physics
RFC enhances humanoid control to imitate complex human motions.
problem Dynamics mismatch between humanoid models and real humans.
method Residual Force Control (RFC) augments control policies with external forces.
result RFC outperforms state-of-the-art methods in convergence speed and motion quality.
Study assesses data-driven and physics-based SGS models for transcritical combustion.
problem Challenges in simulating high-pressure combustion systems due to complex fluid behaviors.
method Comparison of physics-based and random forest machine learning models in turbulent transcritical non-premixed flames.
result Random forest models can effectively model subgrid stresses, providing insight into their formation.
Sig-PCA integrates model outputs and observations to correct model biases.
problem Improving model accuracy and reliability by correcting biases and numerical approximations.
method Sig-PCA framework that combines summary statistics from model outputs with localized observations via a neural network.
result Corrects model outputs to align closely with observational data, preserving essential statistical information.
Optimal sensor placement minimizes information loss from simulations.
problem Designing efficient sensor networks for spatiotemporal processes.
method Model-based sensor placement criterion with sparse variational inference and Gauss-Markov priors.
result Our method identifies sensor networks that minimize information loss from simulated data.
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
problem Solving inverse problems with indirect measurements and uncertainties.
method Bayesian Deep Learning, using neural networks and approximate computations.
result Effective solutions for inverse problems using Bayesian Deep Learning.
Dual ML approach predicts peak temperatures in AFSD, improving process optimization.
problem Lack of understanding between process parameters and resulting microstructure in AFSD.
method Combines supervised machine learning and physics-informed neural networks.
result Ensemble techniques like gradient boosting outperform other SML methods in predicting peak temperatures.
PID-GAN uses physics knowledge to improve deep learning models' reliability.
problem Improving deep learning models' reliability in physics-based applications.
method Physics-informed GAN architecture that incorporates physics knowledge into both generator and discriminator models.
result PID-GAN framework outperforms state-of-the-art in handling gradient imbalance.
Novel digital twin for complex systems improves performance.
problem Lack of practical implementation details for stochastic nonlinear MDOF systems.
method Decouples time-scales, uses physics-based model, Bayesian filtering, and machine learning.
result Excellent performance of proposed digital twin framework validated by examples.
Residual generation helps diagnose engine faults using neural networks.
problem Fault diagnosis in engines with unknown classes and limited data.
method Grey-box recurrent neural networks incorporating physical insights.
result Improved fault classification and root cause identification.
Deep neural network improves amino acid side chain prediction accuracy.
problem Predicting amino acid side chain conformation for protein modeling and design.
method Deep neural network architecture without physics-based assumptions.
result Improved accuracy by more than 25% for aromatic residues.
A new SL strategy optimizes HVAC DR in multi-zone buildings.
problem Optimal DR of HVAC units in multi-zone buildings is challenging.
method Supervised learning with ANN replication and DNN integration.
result SLAMP achieves effective DR schedules with reduced computation time.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
A hybrid ML method improves ship response predictions across different sea conditions.
problem Improving accuracy and generalizability of ML methods for ship response predictions.
method A hybrid machine learning method that corrects forces in a low-fidelity equation of motion.
result The hybrid method offers improved prediction accuracy and generalizability compared to benchmarks.
Physics-guided model improves deep learning for nonlinear systems.
problem Intractable inference of nonlinear dynamical systems from data.
method Physics-guided Deep Markov Model (PgDMM) using neural networks.
result Improved performance on nonlinear systems with structured latent space.
A new method for adversarial attacks using physical parameters like lighting and geometry.
problem Vulnerability of machine learning image classifiers to adversarial attacks.
method Directly perturbs physical parameters (lighting and geometry) instead of pixel colors, using a differentiable renderer.
result Proposes parametric norm-balls for evaluating adversarial attacks, enabling physically-based attacks.
Generative models improve digital twins for structures with uncertainties.
problem Uncertainty in structural modelling limits deterministic models.
method Two types of generative models: physics-based SFE and data-driven cGANs.
result Data-driven cGANs outperform physics-based models in nonlinear structures.
Paper learns predictive ROMs for combustion from high-fidelity simulations.
problem Predicting combustion dynamics from high-fidelity models.
method Combines physics-based model reduction and machine learning.
result ROMs accurately predict combustion dynamics with significant speedup.