
A Differentiable Contact Model to Extend Lagrangian and Hamiltonian Neural Networks for Modeling Hybrid Dynamics
The incorporation of appropriate inductive bias plays a critical role in...
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Physical Symmetries Embedded in Neural Networks
Neural networks are a central technique in machine learning. Recent year...
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Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying sy...
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Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
In this work, we introduce Dissipative SymODEN, a deep learning architec...
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TimeContinuous EnergyConservation Neural Network for Structural Dynamics Analysis
Fast and accurate structural dynamics analysis is important for structur...
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Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider
Using deep neural networks for identifying physics objects at the Large ...
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GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems
We propose the GENERIC formalism informed neural networks (GFINNs) that ...
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Benchmarking EnergyConserving Neural Networks for Learning Dynamics from Data
The last few years have witnessed an increased interest in incorporating physicsinformed inductive bias in deep learning frameworks. In particular, a growing volume of literature has been exploring ways to enforce energy conservation while using neural networks for learning dynamics from observed timeseries data. In this work, we present a comparative analysis of the energyconserving neural networks  for example, deep Lagrangian network, Hamiltonian neural network, etc.  wherein the underlying physics is encoded in their computation graph. We focus on ten neural network models and explain the similarities and differences between the models. We compare their performance in 4 different physical systems. Our result highlights that using a highdimensional coordinate system and then imposing restrictions via explicit constraints can lead to higher accuracy in the learned dynamics. We also point out the possibility of leveraging some of these energyconserving models to design energybased controllers.
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