ξ-torch simplifies physics-informed learning by providing differentiable functionals.
problem Training physics-informed deep neural networks requires differentiable physical simulations.
method ξ-torch offers a library of differentiable functionals for scientific simulations.
result Improves numerical stability and reduces memory requirements for higher order derivatives.
Framework uses probabilistic programming for physics simulation in games.
problem Efficiently simulate physics in games using probabilistic programming.
method Combines model-free and model-based approaches to improve efficiency.
result Model outperforms model-free or model-based approaches alone.
DiffTaichi enables fast, differentiable physical simulations with shorter code.
problem Building efficient differentiable physical simulators.
method Differentiable programming language (DiffTaichi) that generates gradients using source code transformations and a light-weight tape.
result Differentiable physical simulators written in DiffTaichi are faster and more concise than existing methods.
Physics-guided deep learning improves CFD for bubbly flow simulations.
problem Accurate CFD prediction of two-phase bubbly flow with high computational efficiency.
method Developed a multi-scale framework with Feature Similarity Measurement (FSM) for error estimation and a physics-guided deep feedforward neural network (DFNN) surrogate model.
result Physics-guided deep learning achieves comparable accuracy to fine-mesh simulations with fast-running feature.
A new method uses physics-informed neural networks to solve reliability analysis problems without simulations.
problem Solving reliability analysis problems without the need for expensive simulations.
method Physics-informed neural networks to learn directly from problem physics.
result Eliminates the need for expensive simulations and achieves highly accurate results.
JAX MD enables differentiable physics simulations for molecular dynamics.
problem Performing efficient and differentiable physics simulations for molecular dynamics.
method Differentiable physics simulation environments, interaction potentials, neural networks, flexible primitives.
result Differentiable physics simulations can be used for meta-optimization and scaling to large particle systems.
Graph Network-based Simulators learn complex physics simulations.
problem Simulating complex physical systems with high accuracy and scalability.
method Graph Network framework for particle dynamics, learned message-passing.
result Model generalizes well to different conditions and scales, robust to hyperparameters.
Seq2seq models predict complex multi-physics systems' time evolution.
problem Predicting the time-evolution of complex multi-physics systems.
method Sequence-to-sequence models applied to multi-physics simulations.
result Seq2seq models accurately emulate complex systems and predict their evolution.
High-precision machine learning reduces particle physics simulations by orders of magnitude.
problem Reducing computational burden in particle physics simulations.
method Developed optimal training strategies and tuned machine learning regressors, including Deep Neural Networks with skip connections and boosted decision trees.
result Significantly reduced computational time by factors of 10^3 to 10^6 over first-principles simulations.
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting …
Physics-informed neural networks simulate radiative transfer efficiently.
problem Simulating radiative transfer accurately and efficiently.
method Physics-informed neural networks trained to minimize radiative transfer equations.
result PINNs provide an easy-to-implement, robust, and accurate method for radiative transfer simulation.
Paper uses deep learning to improve thermal-hydraulic simulations.
problem Limited credibility of thermal-hydraulic codes in real plant conditions.
method Feature Similarity Measurement (FSM) and deep learning.
result Deep learning constructs relationships between local physical features and simulation errors.
PhyDNN uses physics knowledge to improve drag force prediction models.
problem Complex physical processes in fluid dynamics are hard to model accurately.
method Physics-guided structural priors and aggregate supervision for deep learning.
result PhyDNN achieves a significant 8.46% improvement in drag force prediction.
Physics-informed neural networks improve baryonic predictions from dark matter simulations.
problem Recreating hydrodynamic simulations from dark matter requires expensive and time-consuming computations.
method Combining neural network architectures with physical constraints and using Kullback-Leibler divergence for prediction comparison.
result Improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the metallicity relation, and preserved scatter.
New sampling scheme improves ML accuracy in physics simulations.
problem Improving accuracy of ML models in physics simulations.
method Taylor-based data sampling scheme for DNNs.
result Reduces error in DNN solutions of ODE systems.
Physics-informed machine learning models improve biomolecular system simulations.
problem Modeling unresolved interactions beyond classical force fields.
method Physics-informed neural networks and operator learning.
result Accurate, mechanistic, generalizable models for long-timescale kinetics.
Physics-informed model reduces RBC simulation costs.
problem Computational infeasibility of direct numerical simulations for turbulent systems.
method Combines CNN and recurrent architecture, penalized with PDEs, uses conformal prediction.
result Significant reduction in computational cost for long-term simulations.
Single model learns physics from diverse data.
problem Lack of universal physics models for diverse applications.
method General Physics Transformer (GPhyT) trained on diverse physics data.
result Single model achieves superior performance across multiple physics domains.
New algorithms improve vascular flow simulations in aortic aneurysms.
problem Limited accuracy of MRI in hemodynamics, patient-specific flow boundary conditions, and CFD's computational demands.
method Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets) integrated with 3D Navier-Stokes equations.
result Improved computational efficiency and good agreement with CFD simulations.
New methods improve precision of LHC measurements.
problem Inference on high-dimensional LHC data is difficult due to complex simulators.
method Simulation-based inference methods combining machine learning and simulator information.
result These techniques have the potential to substantially improve LHC measurements.
Optimal transport calibrates machine learning models for particle physics simulations.
problem Discrepancies between simulation and experimental data limit machine learning effectiveness.
method A model calibration approach based on optimal transport applied to high-dimensional simulations.
result Calibrated high-dimensional representations enable proper calibration of various downstream quantities.
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%.
Enhances neural operators with physics knowledge for more accurate simulations.
problem Improving accuracy and generalization of neural operators for physical systems.
method Jointly learns from original PDEs and simplified forms, incorporating fundamental physics.
result Significant improvement in nRMSE across various PDE problems.
Graph Neural Networks model 3D granular flow simulations.
problem Accurate modeling of complex 3D granular flow processes.
method Graph Neural Networks approach to simulate 3D granular flow using LIGGGHTS.
result Machine learning trajectories match physical granular flow processes.
DPC uses physics and neural nets to solve SDEs.
problem Solving stochastic differential equations with missing physics.
method Physics-data fusion with conditional maximum mean discrepancy (CMMD) loss.
result DPC achieves highly accurate solutions on benchmark examples.
DCTR uses neural networks to improve particle physics simulations and parameter tuning.
problem High computational cost limits precise scientific analysis in particle physics.
method Deep neural networks for reweighting and parameter tuning of simulations.
result DCTR enables precise simulations and parameter tuning, improving model accuracy.
Framework learns physics-informed continuum models from molecular data.
problem Discovering accurate and robust data-driven continuum models from molecular simulation data.
method Operator regression framework using neural networks in modal space with physical inductive biases.
result Learned operators generalize to unseen system characteristics.
IsoGCNs learn invariant and equivariant graph features for efficient simulations.
problem Learning isometric transformation invariant and equivariant features in graphs for simulations.
method Transformation invariant and equivariant Graph Convolutional Networks (IsoGCNs).
result IsoGCNs outperform state-of-the-art methods on geometrical and physical simulation tasks.
Physics-Informed Neural Network improves option pricing accuracy.
problem Improving option pricing accuracy using machine learning.
method Physics-Informed Neural Network (PINN) applied to Black-Scholes equation.
result PINN model accurately captures option pricing behavior on both simulated and real market data.
Compact models learn photocurrent dynamics from radiation-induced excess carrier density.
problem Accurate but computationally expensive physics-based photocurrent models for semiconductor devices.
method Dynamic Mode Decomposition (DMD) for learning reduced order models from internal state data.
result Physics-aware, compact delayed photocurrent models accurately approximate internal excess carrier dynamics.
Paper predicts turbulent flows using physics-informed deep learning.
problem Predicting turbulent flows from fluid simulations.
method Hybrid approach combining RANS and LES with trainable spectral filters and U-net.
result Significant reduction in prediction error for 60 frames ahead.
Researchers analyze a new neural network training method.
problem Training robust configurations in discrete weight neural networks.
method Replicated simulated annealing combining physics and classical simulated annealing.
result Explicit criteria for algorithm convergence and successful sampling.
Paper tackles sim-to-real domain adaptation in HEP.
problem Discrepancy between simulations and real data affects ML algorithms performance.
method Domain Adversarial Neural Network trained on HEP data.
result Ensures consistent ML algorithm performance on simulated and real HEP datasets.
Two methods use simulation to improve anomaly detection in particle physics.
problem Artificial bumps in invariant mass spectra from machine learning classifiers.
method Simulation-assisted decorrelation techniques.
result Both methods are robust to correlations in the data and improve anomaly detection.
Improved GAN for chaotic systems reduces training time and captures statistics.
problem Training GANs for chaotic dynamical systems is challenging and often fails to capture true statistics.
method Statistical constraints enforced in GANs to improve model fidelity and reduce training time.
result Statistical regularization leads to better performance in capturing physical properties and significantly reduced training time.
Stochastic approach improves neural network training for kinetic simulations.
problem Training neural networks under physical constraints in kinetic fusion simulations.
method Stochastic augmented Lagrangian approach using pyTorch.
result Higher model prediction accuracy achieved compared to fixed penalty method.
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
A new method combines multifidelity techniques to improve model accuracy with limited data.
problem Improving model accuracy with sparse accurate observations and stochastic simulation models.
method Combines bifidelity and CoKriging methods to estimate empirical statistics and construct a Gaussian process.
result Preserves linear physical constraints up to an error bound, leading to accurate model construction.
TensorNetwork simplifies tensor network algorithms for physics and machine learning.
problem Sparse data structures for quantum physics and machine learning.
method Open-source library for tensor network algorithms.
result Demonstrates applications in physics and machine learning.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
New MCMC method speeds up quantum physics simulations by a factor of 100.
problem Simulating quantum many-body systems with high computational complexity.
method FFT-accelerated MCMC with coupled particle and auxiliary variables.
result Achieves O(NlogN) scaling, significantly faster than traditional O(N3) methods. A new hybrid approach combines physics and machine learning for porous media transport.
problem Simulating 2-phase immiscible transport in porous media.
method Physics-informed deep learning with adversarial neural networks and automatic differentiation.
result The model accurately simulates shock and rarefaction phenomena with limited data.
Sparse matrix decomposition identifies key design variables for ICF experiments.
problem Improving predictive capability of ICF simulation codes through better understanding of design inputs and outcomes.
method Sparse Principal Component Analysis (SPCA) and Random Forest (RF) surrogate model.
result Identified clusters of design variables related to physical processes, revealing important variables not previously considered.
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this …
The paper uses optimal transport to calibrate stochastic simulations.
problem Improper fidelity of stochastic simulators in scientific applications.
method Optimal transport theory applied to neural network corrections.
result Calibrated stochastic simulations improve fidelity to reality.
A new machine learning method handles nuisance parameters for better unfolding in particle physics.
problem Improving statistical correction of cross sections in complex particle physics detectors.
method Profile OmniFold, a machine learning-based Expectation-Maximization procedure that incorporates nuisance parameters.
result Demonstrated the effectiveness of Profile OmniFold on both simulated and real data.
Paper learns particle dynamics for versatile object manipulation.
problem Challenges in traditional rigid-body physics engines for complex scenes.
method Combines learning with particle-based systems for versatile object manipulation.
result Robots achieve complex manipulation tasks using the learned simulator.
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution …