6 papers
CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
Pietro Zanotta, Dibakar Roy Sarkar, Honghui Zheng +2
Controlling partial differential equations (PDEs) with learning-based policies remains fundamentally limited by fixed-dimensional representations: policies trained for a specific s…
Multimodal Neural Operators for Real-Time Biomechanical Modelling of Traumatic Brain Injury
Anusha Agarwal, Dibakar Roy Sarkar, Somdatta Goswami
Background: Traumatic brain injury modeling requires integrating volumetric neuroimaging, demographic parameters, and acquisition metadata. Finite element solvers are too computati…
Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators
Dibakar Roy Sarkar, Ján DrgoÅa, Somdatta Goswami
We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates Time-Integrated Deep Operator Networks (TI-DeepONets) as differentiabl…
Learning Hidden Physics and System Parameters with Deep Operator Networks
Dibakar Roy Sarkar, Vijay Kag, Birupaksha Pal +1
Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data…
ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education
Yue Luo, Dibakar Roy Sarkar, Rachel Herring Sangree +1
Developing effective, domain-specific educational support systems is central to advancing AI in education. Although large language models (LLMs) demonstrate remarkable capabilities…
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…