Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Nayek, Rajdip"'
The growing demand for accurate, efficient, and scalable solutions in computational mechanics highlights the need for advanced operator learning algorithms that can efficiently handle large datasets while providing reliable uncertainty quantification
Externí odkaz:
http://arxiv.org/abs/2409.10972
We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) using R\'enyi's $\alpha$-divergence to learn complex operators while quantifying uncertainty. By incorporating Bayesian neural n
Externí odkaz:
http://arxiv.org/abs/2408.00681
The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods. However, most of the existing neural operators lack the capability to pro
Externí odkaz:
http://arxiv.org/abs/2404.15618
The transformative impact of machine learning, particularly Deep Learning (DL), on scientific and engineering domains is evident. In the context of computational fluid dynamics (CFD), Physics-Informed Neural Networks (PINNs) represent a significant i
Externí odkaz:
http://arxiv.org/abs/2404.03542
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variational Bayes theory, and sparse learning. We propose the extended Krame
Externí odkaz:
http://arxiv.org/abs/2306.15873
The discovery of partial differential equations (PDEs) is a challenging task that involves both theoretical and empirical methods. Machine learning approaches have been developed and used to solve this problem; however, it is important to note that e
Externí odkaz:
http://arxiv.org/abs/2306.04894
Autor:
Mathpati, Yogesh Chandrakant, More, Kalpesh Sanjay, Tripura, Tapas, Nayek, Rajdip, Chakraborty, Souvik
We propose a novel model agnostic data-driven reliability analysis framework for time-dependent reliability analysis. The proposed approach -- referred to as MAntRA -- combines interpretable machine learning, Bayesian statistics, and identifying stoc
Externí odkaz:
http://arxiv.org/abs/2212.06303
Publikováno v:
In Mechanical Systems and Signal Processing 1 July 2024 216
This paper presents the use of spike-and-slab (SS) priors for discovering governing differential equations of motion of nonlinear structural dynamic systems. The problem of discovering governing equations is cast as that of selecting relevant variabl
Externí odkaz:
http://arxiv.org/abs/2012.01937
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2024 418 Part A