6 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…
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…
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…
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…
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…