2 code implementations • 31 May 2024 • Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Iryna Burak, Erik Lien Bolager, Anna Veselovska, Massimo Fornasier, Felix Dietrich
Using neural networks as an ansatz for the solution has proven a challenge in terms of training time and approximation accuracy.
no code implementations • 26 Apr 2024 • Joel Wolfrath, Abhishek Chandra
MinMax sampling is a technique for downsampling a real-valued vector which minimizes the maximum variance over all vector components.
no code implementations • 23 Aug 2023 • Abhishek Chandra, Taniya Kapoor, Bram Daniels, Mitrofan Curti, Koen Tiels, Daniel M. Tartakovsky, Elena A. Lomonova
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems.
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).
1 code implementation • 10 Feb 2023 • Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques.