Advances in Physics-Informed Neural Networks for Simulation and Modeling

The field of physics-informed neural networks is rapidly advancing, with a focus on improving simulation accuracy and efficiency. Recent developments have seen the integration of trajectory-level meta-learning, Hamiltonian dynamics, and waveform iteration to enhance the performance of graph neural simulators and other models. These innovations enable better capture of long-range interactions, non-conservative effects, and complex dynamics, making them more suitable for real-world applications such as robotic manipulation, manufacturing optimization, and lunar landing missions. Notable papers include:

  • Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning, which frames mesh-based simulation as a trajectory-level meta-learning problem to enable rapid adaptation to new simulation scenarios.
  • Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes, which provides a fully Bayesian scheme for estimating probability densities of unknown hidden states and GP hyperparameters from input-output data.
  • Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators, which proposes a parameter-efficient conditioning mechanism to make graph network-based simulators adaptive to material parameters.

Sources

Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes

Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators

A waveform iteration implementation for black-box multi-rate higher-order coupling

Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning

Stuart-Landau Oscillatory Graph Neural Network

Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics

NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos

Learning based Modelling of Throttleable Engine Dynamics for Lunar Landing Mission

Dynamic Modeling and Control of Phosphate-Pebble Drying Systems - A Comprehensive Approach

Dynamic Modeling and Control of Phosphate-Pebble Drying Systems -- A Comprehensive Approach

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