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.
Paper introduces a method to generate physically feasible dynamics with physical priors.
problem Challenges in generating physically feasible dynamics under physical priors.
method Seamlessly incorporates physical priors into diffusion-based generative models.
result Efficient generation of physically realistic dynamics across various physical phenomena.
Physics-informed deep models handle uncertainty in complex systems.
problem Uncertainty in physical systems due to randomness and noise.
method Physics-informed variational inference for deep generative models.
result Scalable framework for characterizing uncertainty in physical systems.
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.
Hybrid model combines physics and data to handle incomplete systems.
problem Incomplete physics models with missing terms.
method Combines deep grey-box models with Optimal Transport.
result Enhances incomplete physics models with superior performance.
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.
Machine learning models emulate and approximate complex mappings in model physics.
problem Developing and ensuring accurate physical parameterizations.
method Machine learning tools to emulate and approximate mappings.
result ML can improve parameterizations and enforce physical constraints.
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.
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.
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.
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.
PICN learns physical fields from shallow neural networks, improving AI in multi-physical systems.
problem Challenges in modeling and forecasting multi-physical systems due to data scarcity and noise.
method Physics-informed convolutional network (PICN) combining CNN and physical laws, using deconvolution and convolution layers.
result PICN effectively solves and estimates nonlinear physical operator equations and recovers physical information from noisy observations.
Physics models integrated into VAEs improve generative performance and extrapolation.
problem Improving generative models with interpretability and robustness.
method Physics-based latent space in VAEs with regularized learning to balance physics and neural network components.
result Generative performance and extrapolation improvements demonstrated on synthetic and real-world datasets.
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.
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.
Framework augments physical models with deep learning for complex dynamics forecasting.
problem Forecasting complex dynamical phenomena with partial knowledge.
method APHYNITY framework: decomposes dynamics into physical and data-driven components.
result Framework accurately forecasts system evolution and identifies relevant parameters.
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.
Graphical physics network learns intuitive physics using deep reinforcement learning with intrinsic motivation.
problem Teaching intuitive physics to AI agents.
method Integrates deep reinforcement learning with intrinsic reward normalization for efficient learning.
result Agent effectively learns object positions and velocities using intrinsic motivation.
Forward-prediction models enhance physical reasoning, but only for specific tasks.
problem Improving physical reasoning in complex tasks involving many objects.
method Incorporated forward-prediction models into simple physical-reasoning agents and evaluated their performance on the PHYRE benchmark.
result Forward-prediction models improve physical-reasoning performance, especially on complex tasks, but generalization to new task templates is challenging.
FunDiff models physical functions using diffusion and autoencoders.
problem Adapting generative models to continuous physical functions.
method Combines latent diffusion with function autoencoder, enforcing physical priors.
result Achieves optimal convergence rates for physical function estimation.
Paper introduces a method to learn physics between digital twins using imperfect models.
problem Learning physics from imperfect data and low-fidelity models.
method Bayesian Hierarchical modeling with physics-informed Gaussian processes.
result Models learning between digital twins are less uncertain than independent models but not over-confident.
MPP trains a transformer to predict multiple physical systems, improving accuracy across various tasks.
problem Training models for specific physical systems is inefficient and requires fine-tuning.
method MPP trains a shared transformer on multiple heterogeneous physical systems, projecting fields into a shared embedding space.
result A single MPP-pretrained transformer outperforms task-specific models on all pretraining sub-tasks and downstream tasks.
Generative model learns wireless channel distributions efficiently.
problem Learning precise wireless channel distributions for optimal communication.
method Physics-informed sparse Bayesian generative modeling (SBGM) with compressed data.
result Model learns channel parameters from compressed AP observations, is physically interpretable, and generalizes across different systems.
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.
Generative models synthesize microstructures respecting physical invariances.
problem Synthesizing microstructures given limited images and physical constraints.
method Three generative models: WGAN, physics-informed GAN, and hybrid model.
result Synthesized microstructures respect physical invariances and latent variable constraints.
Quantum physics model uses knot theory for fragile topology.
problem Modeling quantum physics' fragile topology.
method Knot theoretic algorithm.
result Quantum physics' fragile topology modeled.
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.
Neural model predicts object states and physical parameters from visual observations.
problem Computational models struggle with physical reasoning and adapting to new environments.
method Visual prior predicts particle-based system from visual observations; inference module refines estimates subject to dynamics constraints.
result Model can infer physical properties within a few observations and adapt to unseen scenarios.
Paper builds a physical model of a foliation theory concept.
problem Describing and visualizing the Reeb foliation.
method Geometric methods for 3D printing.
result First comprehensive physical model of the Reeb foliation.
p3VAE combines physics and machine learning for robust data representations.
problem Improving machine learning models' robustness to environmental factors of variation.
method Physics-informed variational autoencoder integrating physical knowledge with neural networks.
result p3VAE outperforms competing models in extrapolation and interpretability. Improves machine learning models by incorporating physical laws into feature maps.
problem Lack of model interpretability in classical machine learning approaches.
method Physics-informed feature maps constructed from physical laws and dimensional analysis.
result Enhanced model interpretability and potential discovery of new physical equations.
A new method learns physical system sensitivity to improve policy learning without needing a full model.
problem Expensive policy learning without models and model bias.
method Learn sensitivity of trajectories to parameter perturbations.
result Feasibility demonstrated on a physical robot.
Bayesian framework calibrates imperfect models using physics-informed priors and Hamiltonian Monte Carlo.
problem Quantifying uncertainty in imperfect computer models described by differential equations.
method Physics-informed Gaussian process priors, discrepancy function, Hamiltonian Monte Carlo, data approximations.
result Framework accurately recovers true parameters and produces accurate predictions.
Physics-informed kernel learning integrates physical priors into machine learning models.
problem Tackles the integration of physical laws into machine learning models for improved accuracy and efficiency.
method Uses Fourier methods to approximate the kernel and minimizes a physics-informed risk function.
result Demonstrates PIKL outperforms physics-informed neural networks and traditional PDE solvers in various scenarios.
New model improves traffic flow predictions with physics and machine learning.
problem Inaccurate predictions in traffic flow modeling with small or noisy datasets.
method Developed a physics regularized Gaussian process (PRGP) model to encode physical models into ML architecture and regularize the training process.
result The PRGP model outperforms previous methods in estimation precision and input robustness.
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.
Neural Physicist learns physical dynamics from images.
problem Learning meaningful physical state representations and accurate state transitions from image sequences.
method Neural Physicist uses VAE for state extraction, NP for parameters, and SSM for dynamics.
result Achieves long-term predictions and identifies system degrees of freedom.
The article develops models for learning and controlling physical systems with unknown inputs.
problem Learning and controlling physical systems with unknown inputs.
method Gaussian process latent force models (GP-LFMs) combining first-principles models and non-parametric GP components.
result Theoretical observability and controllability results for GP-LFMs.
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.
Methods estimate physical load and fatigue from wearable data.
problem Estimating physical load and fatigue from wearable data.
method Statistical and machine learning methods based on kurtosis-skewness diagram.
result Different levels of physical activities and fitness can be distinguished from the kurtosis-skewness diagram.
Physics-informed neural networks improve model accuracy and efficiency.
problem Accurate dynamic models for technical systems are hard to achieve.
method Physics-informed neural ordinary differential equations (PINODE) integrating Lagrangian mechanics.
result Hybrid model combines physical insight and data approximation.
Physics-informed IFT models physical systems with uncertainty, independent of numerical schemes.
problem Modeling physical systems with unknown elements like missing parameters and noisy data.
method Physics-informed Information Field Theory (PIFT) that combines measurements with physical laws, independent of numerical schemes.
result PIFT can capture multiple modes and solve ill-posed problems, robust to model-form uncertainty.
A model learns object representations for physical scene understanding without direct supervision.
problem Learning object-centric representations without direct supervision of object properties.
method Object-Oriented Prediction and Planning (O2P2) model that learns perception, physics interaction, and rendering functions.
result The model can predict physical interactions and build block towers more complex than those seen during training.
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.
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.
This work combines machine learning with physical models to solve inverse problems efficiently.
problem Solving inverse problems in the presence of missing physics and recovering parameters.
method Variational autoencoding with a physically structured decoder network and stochastic local approximations.
result The method accelerates inference for Bayesian inverse problems and acts as a regularizer encoding prior physical information.
PhICNet combines physics and deep learning for forecasting and source identification in dynamical systems.
problem Forecasting and identifying unobservable external sources in spatio-temporal dynamical systems.
method Physics-Incorporated Convolutional Recurrent Neural Network (PhICNet).
result PhICNet can forecast dynamics and identify sources for relatively long periods.
PGA neural network improves uncertainty quantification in lake temperature modeling.
problem Quantifying uncertainties in lake temperature models while maintaining physical consistency.
method Integrates physical constraints into neural networks using Monte Carlo Dropout.
result Ensures better generalizability and physical consistency in MC estimates.