Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Kapoor, Taniya"'
Autor:
Datar, Chinmay, Kapoor, Taniya, Chandra, Abhishek, Sun, Qing, Burak, Iryna, Bolager, Erik Lien, Veselovska, Anna, Fornasier, Massimo, Dietrich, Felix
Approximation of solutions to partial differential equations (PDE) is an important problem in computational science and engineering. Using neural networks as an ansatz for the solution has proven a challenge in terms of training time and approximatio
Externí odkaz:
http://arxiv.org/abs/2405.20836
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations. Specifically, Euler-Bernoulli and Timoshenko beam models on the Winkler foundation are simulated using a transfer learning approach within a causal
Externí odkaz:
http://arxiv.org/abs/2311.00578
Autor:
Chandra, Abhishek, Kapoor, Taniya, Daniels, Bram, Curti, Mitrofan, Tiels, Koen, Tartakovsky, Daniel M., Lomonova, Elena A.
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We develop an ordinary differential equation-based recurrent neural networ
Externí odkaz:
http://arxiv.org/abs/2308.12002
Autor:
Kapoor, Taniya, Chandra, Abhishek, Tartakovsky, Daniel M., Wang, Hongrui, Nunez, Alfredo, Dollevoet, Rolf
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). This paper aims to enhance
Externí odkaz:
http://arxiv.org/abs/2308.08989
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 (PINNs) utilize the underlying physics of moving load problems and aim to pre
Externí odkaz:
http://arxiv.org/abs/2304.00369
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2023
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 wit
Externí odkaz:
http://arxiv.org/abs/2303.01055
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part A
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; 2024, Vol. 35 Issue: 5 p5981-5995, 15p
Autor:
Kapoor, Taniya, Haripriya, A.
Publikováno v:
Indian Journal of Nutrition & Dietetics; Oct-Dec2020, Vol. 57 Issue 4, p422-438, 17p