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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...
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06/08/2023
A Bayesian Framework for learning governing Partial Differential Equation from Data
The discovery of partial differential equations (PDEs) is a challenging ...
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02/12/2023
Physics informed WNO
Deep neural operators are recognized as an effective tool for learning s...
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02/09/2023
Discovering interpretable Lagrangian of dynamical systems from data
A complete understanding of physical systems requires models that are ac...
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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...
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12/13/2022
MAntRA: A framework for model agnostic reliability analysis
We propose a novel model agnostic data-driven reliability analysis frame...
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11/23/2022
Model-agnostic stochastic model predictive control
We propose a model-agnostic stochastic predictive control (MASMPC) algor...
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08/11/2022
Learning governing physics from output only measurements
Extracting governing physics from data is a key challenge in many areas ...
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08/11/2022
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Operator learning frameworks, because of their ability to learn nonlinea...
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05/04/2022
Wavelet neural operator: a neural operator for parametric partial differential equations
With massive advancements in sensor technologies and Internet-of-things,...
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08/24/2021
Generalized weakly corrected Milstein solutions to stochastic differential equations
In this work, weakly corrected explicit, semi-implicit and implicit Mils...
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08/24/2021
