1 citations · 1 across the 9 of their papers we have counts for
9 papers
Neural Hodge Corrective Solvers: A Hybrid Iterative-Neural Framework
Arjun Puthli, Somdatta Goswami, Souvik Chakraborty
We introduce the Neural Hodge Corrective Solver (NHCS), a hybrid iterative-neural framework for partial differential equations that embeds learned corrective operators within the D…
CortiNet: A Physics-Perception Hybrid Cortical-Inspired Dual-Stream Network for Gallbladder Disease Diagnosis from Ultrasound
Vagish Kumar, Souvik Chakraborty
Ultrasound imaging is the primary diagnostic modality for detecting Gallbladder diseases due to its non-invasive nature, affordability, and wide accessibility. However, the low res…
Event-driven physics-informed operator learning for reliability analysis
Shailesh Garg, Souvik Chakraborty
Reliability analysis of engineering systems under uncertainty poses significant computational challenges, particularly for problems involving high-dimensional stochastic inputs, no…
NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
Shailesh Garg, Souvik Chakraborty
We introduce NeuroPINNs, a neuroscience-inspired extension of Physics-Informed Neural Networks (PINNs) that incorporates biologically motivated spiking neuron models to achieve ene…
Competition is the key: A Game Theoretic Causal Discovery Approach
Amartya Roy, Souvik Chakraborty
Causal discovery remains a central challenge in machine learning, yet existing methods face a fundamental gap: algorithms like GES and GraN-DAG achieve strong empirical performance…
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning
Isha Jain, Shailesh Garg, Shaurya Shriyam +1
Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures…
Distribution free uncertainty quantification in neuroscience-inspired deep operators
Shailesh Garg, Souvik Chakraborty
Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are…
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
Shailesh Garg, Souvik Chakraborty
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intellig…
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…