1 citations · 1 across the 4 of their papers we have counts for
4 papers
ANCHOR: Error-Controlled Adaptive Numerical Correction for Neural Operator Time Marching
Rajyasri Roy, Dibyajyoti Nayak, Somdatta Goswami
Numerical simulation of time-dependent partial differential equations (PDEs) is central to scientific and engineering applications, but high-fidelity solvers are often prohibitivel…
Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators
Dibyajyoti Nayak, Somdatta Goswami
Developing accurate, data-efficient surrogate models is central to advancing AI for Science. Neural operators (NOs), which approximate mappings between infinite-dimensional functio…
Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference
Luis Mandl, Dibyajyoti Nayak, Tim Ricken +1
Accurately modeling and inferring solutions to time-dependent partial differential equations (PDEs) over extended horizons remains a core challenge in scientific machine learning.…
TI-DeepONet: Learnable Time Integration for Stable Long-Term Extrapolation
Dibyajyoti Nayak, Somdatta Goswami
Accurate temporal extrapolation remains a fundamental challenge for neural operators modeling dynamical systems, where predictions must extend far beyond the training horizon. Conv…