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Found 5 papers, 1 papers with code
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations.
Neural oscillators for magnetic hysteresis modeling
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems.
Neural oscillators for generalization of physics-informed machine learning
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs).
Physics-informed machine learning for moving load problems
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML).
Physics-informed neural networks for solving forward and inverse problems in complex beam systems
This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theory, where the double beams are connected with a Winkler foundation.
