no code implementations • 1 Nov 2023 • Taniya Kapoor, Hongrui Wang, Alfredo Nunez, Rolf Dollevoet
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations.
1 code implementation • 17 Aug 2023 • Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Nunez, Rolf Dollevoet
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).
no code implementations • 1 Apr 2023 • Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML).
no code implementations • 2 Mar 2023 • Taniya Kapoor, Hongrui Wang, Alfredo Nunez, Rolf Dollevoet
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.