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This review highlights applications of physics-informed neural networks in digital twins and autonomy, indicating potential for materials discovery.
This talk reviews physics-informed neural networks and introduces networks that learn functionals and nonlinear operators for system identification. It discusses applications in digital twins, autonomy, and materials discovery, and introduces bio-inspired solutions like spiking neural networks and neuromorphic computing.
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George Em Karniadakis (2025) studied this question.
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