
Physical Symmetries Embedded in Neural Networks
Neural networks are a central technique in machine learning. Recent year...
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PhysicsInformed MultiLSTM Networks for Metamodeling of Nonlinear Structures
This paper introduces an innovative physicsinformed deep learning frame...
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NeuroSymbolic Constraint Programming for Structured Prediction
We propose Nester, a method for injecting neural networks into constrain...
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Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics
For multilayer materials in thin substrate systems, interfacial failure ...
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AI Feynman: a PhysicsInspired Method for Symbolic Regression
A core challenge for both physics and artificial intellicence (AI) is sy...
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PDENetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
Bridging physics and deep learning is a topical challenge. While deep le...
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Parsimonious neural networks learn classical mechanics, its underlying symmetries, and an accurate time integrator
Machine learning is playing an increasing role in the physical sciences ...
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Physical Constraint Embedded Neural Networks for inference and noise regulation
Neural networks often require large amounts of data to generalize and can be illsuited for modeling small and noisy experimental datasets. Standard network architectures trained on scarce and noisy data will return predictions that violate the underlying physics. In this paper, we present methods for embedding even–odd symmetries and conservation laws in neural networks and propose novel extensions and use cases for physical constraint embedded neural networks. We design an even–odd decomposition architecture for disentangling a neural network parameterized function into its even and odd components and demonstrate that it can accurately infer symmetries without prior knowledge. We highlight the noise resilient properties of physical constraint embedded neural networks and demonstrate their utility as physicsinformed noise regulators. Here we employed a conservation of energy constraint embedded network as a physicsinformed noise regulator for a symbolic regression task. We showed that our approach returns a symbolic representation of the neural network parameterized function that aligns well with the underlying physics while outperforming a baseline symbolic regression approach.
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