124 citations · 190 across the 14 of their papers we have counts for
14 papers
Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction
Rohan Vitthal Thorat, Rajdip Nayek
Accurate prediction of structural dynamics is imperative for preserving digital twin fidelity throughout operational lifetimes. Parametric models with fixed nominal parameters ofte…
Probabilistic Digital Twin for Misspecified Structural Dynamical Systems via Latent Force Modeling and Bayesian Neural Networks
Sahil Kashyap, Rajdip Nayek
This work presents a probabilistic digital twin framework for response prediction in dynamical systems governed by misspecified physics. The approach integrates Gaussian Process La…
Safe Reinforcement Learning-Based Vibration Control: Overcoming Training Risks with LQR Guidance
Rohan Vitthal Thorat, Juhi Singh, Rajdip Nayek
Structural vibrations induced by external excitations pose significant risks, including safety hazards for occupants, structural damage, and increased maintenance costs. While conv…
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
Sawan Kumar, Tapas Tripura, Rajdip Nayek +1
Operator learning offers a powerful paradigm for solving parametric partial differential equations (PDEs), but scaling probabilistic neural operators such as the recently proposed…
A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading
Toiba Noor, Soban Nasir Lone, G. V. Ramana +1
In geotechnical engineering, constitutive models are central to capturing soil behavior across diverse drainage conditions, stress paths,and loading histories. While data driven de…
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
The growing demand for accurate, efficient, and scalable solutions in computational mechanics highlights the need for advanced operator learning algorithms that can efficiently han…
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification
Soban Nasir Lone, Subhayan De, Rajdip Nayek
We introduce a novel deep operator network (DeepONet) framework that incorporates generalised variational inference (GVI) using Rényi's -divergence to learn complex operators wh…
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
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. How…
Physics Informed Neural Networks for Free Shear Flows
Siddharth Raghu, Rajdip Nayek, Vamsi Chalamalla
The transformative impact of machine learning, particularly Deep Learning (DL), on scientific and engineering domains is evident. In the context of computational fluid dynamics (CF…
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…
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…
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
Rajdip Nayek, Ramon Fuentes, Keith Worden +1
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 discove…
A Gaussian process latent force model for joint input-state estimation in linear structural systems
Rajdip Nayek, Souvik Chakraborty, Sriram Narasimhan
The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel method…