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Physics-Informed Neural Networks and Extensions
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In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations.
Forward citations
Cited by 3 Pith papers
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The Ramanujan Challenge For AI
A benchmark of new continued fractions, recurrences, series and integrals for classical constants, split into encrypted-proof problems and open conjectures for testing AI mathematical reasoning.
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Can AI Follow In Einstein's Footsteps?
AI for physics has moved from explicit equation discovery to black-box prediction, a trajectory the authors argue reverses the historical progression of human physics, leaving the invention of new mathematical framewo...
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Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
A physics-informed neural network conditioned on solar wind parameters reconstructs the 3D magnetic field of Mars's induced magnetosphere.
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