We propose a novel framework for discovering Stochastic Partial Differen...
The discovery of partial differential equations (PDEs) is a challenging ...
Deep neural operators are recognized as an effective tool for learning
s...
A complete understanding of physical systems requires models that are
ac...
In this paper, we propose a novel data-driven operator learning framewor...
Machine learning (ML) and Artificial Intelligence (AI) are increasingly ...
A framework for creating and updating digital twins for dynamical system...
We propose a novel model agnostic data-driven reliability analysis frame...
U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (D...
Computationally efficient and trustworthy machine learning algorithms ar...
We propose a model-agnostic stochastic predictive control (MASMPC) algor...
To understand the potential of intelligent confirmatory tools, the U.S.
...
We propose a stochastic projection-based gradient free physics-informed
...
Extracting governing physics from data is a key challenge in many areas ...
Operator learning frameworks, because of their ability to learn nonlinea...
Neural network based data-driven operator learning schemes have shown
tr...
With massive advancements in sensor technologies and Internet-of-things,...
State estimation is required whenever we deal with high-dimensional dyna...
Time-dependent structural reliability analysis of nonlinear dynamical sy...
Time dependent reliability analysis and uncertainty quantification of
st...
We propose a new hybrid topology optimization algorithm based on multigr...
We propose a novel capsule based deep encoder-decoder model for
surrogat...
We propose a novel deep learning based surrogate model for solving
high-...
We propose a deep learning-based surrogate model for stochastic simulato...
For real-life nonlinear systems, the exact form of nonlinearity is often...
In this work, weakly corrected explicit, semi-implicit and implicit Mils...
The present study utilizes the Girsanov transformation based framework f...
The physical world is governed by the laws of physics, often represented...
Performing reliability analysis on complex systems is often computationa...
The potential of digital twin technology is immense, specifically in the...
The importance of state estimation in fluid mechanics is well-establishe...
For many systems in science and engineering, the governing differential
...
Digital twin technology has a huge potential for widespread applications...
This paper presents a simulation free framework for solving reliability
...
Digital twin technology has significant promise, relevance and potential...
We present a new physics informed neural network (PINN) algorithm for so...
We present a novel approach, referred to as the 'threshold shift method'...
The problem of combined state and input estimation of linear structural
...