242 citations · 629 across the 42 of their papers we have counts for
42 papers
A dual physics-informed neural network for topology optimization
Ajendra Singh, Souvik Chakraborty, Rajib Chowdhury
We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based t…
Revealing the Predictive Power of Neural Operators for Strain Evolution in Digital Composites
Meer Mehran Rashid, Souvik Chakraborty, N. M. Anoop Krishnan
The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical co…
Discovering stochastic partial differential equations from limited data using variational Bayes inference
Yogesh Chandrakant Mathpati, Tapas Tripura, Rajdip Nayek +1
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variat…
A Bayesian Framework for learning governing Partial Differential Equation from Data
Kalpesh More, Tapas Tripura, Rajdip Nayek +1
The discovery of partial differential equations (PDEs) is a challenging task that involves both theoretical and empirical methods. Machine learning approaches have been developed a…
Physics informed WNO
Navaneeth N, Tapas Tripura, Souvik Chakraborty
Deep neural operators are recognized as an effective tool for learning solution operators of complex partial differential equations (PDEs). As compared to laborious analytical and…
Discovering interpretable Lagrangian of dynamical systems from data
Tapas Tripura, Souvik Chakraborty
A complete understanding of physical systems requires models that are accurate and obeys natural conservation laws. Recent trends in representation learning involve learning Lagran…
Randomized prior wavelet neural operator for uncertainty quantification
Shailesh Garg, Souvik Chakraborty
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an e…
Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
Qianying Cao, Somdatta Goswami, Tapas Tripura +2
The use of neural operators in a digital twin model of an offshore floating structure can provide a paradigm shift in structural response prediction and health monitoring, providin…
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
Tapas Tripura, Aarya Sheetal Desai, Sondipon Adhikari +1
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to upd…
MAntRA: A framework for model agnostic reliability analysis
Yogesh Chandrakant Mathpati, Kalpesh Sanjay More, Tapas Tripura +2
We propose a novel model agnostic data-driven reliability analysis framework for time-dependent reliability analysis. The proposed approach -- referred to as MAntRA -- combines int…
Model-agnostic stochastic model predictive control
Tapas Tripura, Souvik Chakraborty
We propose a model-agnostic stochastic predictive control (MASMPC) algorithm for dynamical systems. The proposed approach first discovers \textit{interpretable} governing different…
AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
Kazuma Kobayashi, Dinesh Kumar, Syed Bahauddin Alam
In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation co…
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
James Daniell, Kazuma Kobayashi, Ayodeji Alajo +1
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can…
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code
Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney +3
To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viab…
Stochastic projection based approach for gradient free physics informed learning
Navaneeth N, Souvik Chakraborty
We propose a stochastic projection-based gradient free physics-informed neural network. The proposed approach, referred to as the stochastic projection based physics informed neura…
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations
Tushar, Souvik Chakraborty
We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corr…
Learning governing physics from output only measurements
Tapas Tripura, Souvik Chakraborty
Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and s…
Multi-fidelity wavelet neural operator with application to uncertainty quantification
Akshay Thakur, Tapas Tripura, Souvik Chakraborty
Operator learning frameworks, because of their ability to learn nonlinear maps between two infinite dimensional functional spaces and utilization of neural networks in doing so, ha…
Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator
Meer Mehran Rashid, Tanu Pittie, Souvik Chakraborty +1
Increased demands for high-performance materials have led to advanced composite materials with complex hierarchical designs. However, designing a tailored material microstructure w…
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Shailesh Garg, Souvik Chakraborty
Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics. DeepONet is one such neural network architecture which has ga…
Wavelet neural operator: a neural operator for parametric partial differential equations
Tapas Tripura, Souvik Chakraborty
With massive advancements in sensor technologies and Internet-of-things, we now have access to terabytes of historical data; however, there is a lack of clarity in how to best expl…
Koopman operator for time-dependent reliability analysis
Navaneeth N., Souvik Chakraborty
Time-dependent structural reliability analysis of nonlinear dynamical systems is non-trivial; subsequently, scope of most of the structural reliability analysis methods is limited…
Energy networks for state estimation with random sensors using sparse labels
Yash Kumar, Souvik Chakraborty
State estimation is required whenever we deal with high-dimensional dynamical systems, as the complete measurement is often unavailable. It is key to gaining insight, performing co…
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Shailesh Garg, Harshit Gupta, Souvik Chakraborty
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates consi…