2 citations · 3 across the 18 of their papers we have counts for
22 papers
Learning Physics from an Imperfect Ancestor
S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas +1
Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependen…
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang +3
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. I…
A Non-Overlapping Schwarz Hybrid Finite Element-Neural Operator Framework for Solid Mechanics on Irregular Domains
Wei Wang, Abhinav Gupta, Haihui Ruan +1
Finite element (FE) methods are the benchmark for solid mechanics simulations, yet their computational cost becomes prohibitive for problems with localised nonlinearities, fine-sca…
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…
Can Coding Agents Reproduce Findings in Computational Materials Science?
Ziyang Huang, Yi Cao, Ali K. Shargh +15
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is uncle…
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…
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
Shailesh Garg, Luis Mandl, Somdatta Goswami +1
Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-co…
A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials
Purna Vindhya Kota, Meer Mehran Rashid, Somdatta Goswami +1
Predicting stress fields in hyperelastic materials with complex microstructures remains challenging for traditional deep learning surrogates, which struggle to capture both sharp s…
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…
Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators
Dibakar Roy Sarkar, Ján Drgoňa, Somdatta Goswami
We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates time-integrated deep operator networks (TI-DeepONet) as differentiable…
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…
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.…
Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs
Ishan Khurjekar, Indrashish Saha, Lori Graham-Brady +1
Systems governed by partial differential equations (PDEs) require computationally intensive numerical solvers to predict spatiotemporal field evolution. While machine learning (ML)…
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…
Enabling Local Neural Operators to perform Equation-Free System-Level Analysis
Gianluca Fabiani, Hannes Vandecasteele, Somdatta Goswami +2
Neural Operators (NOs) provide a powerful framework for computations involving physical laws that can be modelled by (integro-) partial differential equations (PDEs), directly lear…
Time Marching Neural Operator FE Coupling: AI Accelerated Physics Modeling
Wei Wang, Maryam Hakimzadeh, Haihui Ruan +1
Numerical solvers for PDEs often struggle to balance computational cost with accuracy, especially in multiscale and time-dependent systems. Neural operators offer a promising way t…
Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEs
Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami
Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between…
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…
Basis-to-Basis Operator Learning Using Function Encoders
Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami +2
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We d…
Causality-Respecting Adaptive Refinement for PINNs: Enabling Precise Interface Evolution in Phase Field Modeling
Wei Wang, Tang Paai Wong, Haihui Ruan +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in…
Efficient Training of Deep Neural Operator Networks via Randomized Sampling
Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami
Neural operators (NOs) employ deep neural networks to learn mappings between infinite-dimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has…