activity
20222024
most citedLearning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator

5 citations · 22 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CE2024

FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling

Tushar, Sawan Kumar, Souvik Chakraborty

Constitutive modelling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to ca…

stat.ML2024

Discovering governing equation in structural dynamics from acceleration-only measurements

Calvin Alvares, Souvik Chakraborty

Over the past few years, equation discovery has gained popularity in different fields of science and engineering. However, existing equation discovery algorithms rely on the availa…

math.DS2024

Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems

Tapas Tripura, Satyam Panda, Budhaditya Hazra +1

Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic exci…

cs.RO2024

PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators

Harshil Vagadia, Mudit Chopra, Abhinav Barnawal +4

Given the task of positioning a ball-like object to a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. Howeve…

cs.LG20245 cited

Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data

Jyoti Rani, Tapas Tripura, Hariprasad Kodamana +1

Fault detection and isolation in complex systems are critical to ensure reliable and efficient operation. However, traditional fault detection methods often struggle with issues su…

cs.NE20231 cited

Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression

Shailesh Garg, Souvik Chakraborty

Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel…