research
∙
06/28/2023
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differen...
share
research
∙
06/08/2023
A Bayesian Framework for learning governing Partial Differential Equation from Data
The discovery of partial differential equations (PDEs) is a challenging ...
research
∙
02/12/2023
Physics informed WNO
Deep neural operators are recognized as an effective tool for learning s...
research
∙
02/09/2023
Discovering interpretable Lagrangian of dynamical systems from data
A complete understanding of physical systems requires models that are ac...
research
∙
02/02/2023
Randomized prior wavelet neural operator for uncertainty quantification
In this paper, we propose a novel data-driven operator learning framewor...
research
∙
01/17/2023
Explainable, Interpretable Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life
Machine learning (ML) and Artificial Intelligence (AI) are increasingly ...
research
∙
12/19/2022
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
A framework for creating and updating digital twins for dynamical system...
research
∙
12/13/2022
MAntRA: A framework for model agnostic reliability analysis
We propose a novel model agnostic data-driven reliability analysis frame...
research
∙
11/24/2022
Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology
U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (D...
research
∙
11/23/2022
Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction
Computationally efficient and trustworthy machine learning algorithms ar...
research
∙
11/23/2022
Model-agnostic stochastic model predictive control
We propose a model-agnostic stochastic predictive control (MASMPC) algor...
research
∙
10/14/2022
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code
To understand the potential of intelligent confirmatory tools, the U.S. ...
research
∙
09/25/2022
Stochastic projection based approach for gradient free physics informed learning
We propose a stochastic projection-based gradient free physics-informed ...
research
∙
08/11/2022
Learning governing physics from output only measurements
Extracting governing physics from data is a key challenge in many areas ...
research
∙
08/11/2022
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Operator learning frameworks, because of their ability to learn nonlinea...
research
∙
06/12/2022
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Neural network based data-driven operator learning schemes have shown tr...
research
∙
05/04/2022
Wavelet neural operator: a neural operator for parametric partial differential equations
With massive advancements in sensor technologies and Internet-of-things,...
research
∙
03/12/2022
Energy networks for state estimation with random sensors using sparse labels
State estimation is required whenever we deal with high-dimensional dyna...
research
∙
03/05/2022
Koopman operator for time-dependent reliability analysis
Time-dependent structural reliability analysis of nonlinear dynamical sy...
research
∙
01/31/2022
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Time dependent reliability analysis and uncertainty quantification of st...
research
∙
01/27/2022
Efficient hybrid topology optimization using GPU and homogenization based multigrid approach
We propose a new hybrid topology optimization algorithm based on multigr...
research
∙
01/19/2022
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification
We propose a novel capsule based deep encoder-decoder model for surrogat...
research
∙
11/08/2021
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
We propose a novel deep learning based surrogate model for solving high-...
research
∙
10/24/2021
A deep learning based surrogate model for stochastic simulators
We propose a deep learning-based surrogate model for stochastic simulato...
research
∙
09/01/2021
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
For real-life nonlinear systems, the exact form of nonlinearity is often...
research
∙
08/24/2021
Generalized weakly corrected Milstein solutions to stochastic differential equations
In this work, weakly corrected explicit, semi-implicit and implicit Mils...
research
∙
08/24/2021
A change of measure enhanced near exact Euler Maruyama scheme for the solution to nonlinear stochastic dynamical systems
The present study utilizes the Girsanov transformation based framework f...
research
∙
08/24/2021
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations
The physical world is governed by the laws of physics, often represented...
research
∙
05/11/2021
Surrogate assisted active subspace and active subspace assisted surrogate – A new paradigm for high dimensional structural reliability analysis
Performing reliability analysis on complex systems is often computationa...
research
∙
03/29/2021
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
The potential of digital twin technology is immense, specifically in the...
research
∙
01/27/2021
State estimation with limited sensors – A deep learning based approach
The importance of state estimation in fluid mechanics is well-establishe...
research
∙
05/19/2020
Transfer learning based multi-fidelity physics informed deep neural network
For many systems in science and engineering, the governing differential ...
research
∙
05/12/2020
Machine learning based digital twin for dynamical systems with multiple time-scales
Digital twin technology has a huge potential for widespread applications...
research
∙
05/04/2020
Simulation free reliability analysis: A physics-informed deep learning based approach
This paper presents a simulation free framework for solving reliability ...
research
∙
01/25/2020
The role of surrogate models in the development of digital twins of dynamic systems
Digital twin technology has significant promise, relevance and potential...
research
∙
07/04/2019
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
We present a new physics informed neural network (PINN) algorithm for so...
research
∙
04/25/2019
Threshold shift method for reliability-based design optimization
We present a novel approach, referred to as the 'threshold shift method'...
research
∙
03/29/2019