3 citations · 4 across the 11 of their papers we have counts for
12 papers
Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems
Mahindra Rautela, Alexander Scheinker, Ayan Biswas +3
Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment fine-tuning optimizes only t…
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
Mahindra Rautela, Alexander Most, Siddharth Mansingh +9
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-dis…
The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality
Roxana Bujack, Li-Ta Lo, Ethan Stam +2
Recent advances in AI enable the automatic generation of visualizations directly from textual prompts using agentic workflows. However, visualizations produced via one-shot generat…
SPUS: A Lightweight and Parameter-Efficient Foundation Model for PDEs
Abu Bucker Siddik, Diane Oyen, Alexander Most +2
We introduce Small PDE U-Net Solver (SPUS), a compact and efficient foundation model (FM) designed as a unified neural operator for solving a wide range of partial differential equ…
Implementation of a 8-bit Wallace Tree Multiplier
Ayan Biswas, Jimmy Jin
Wallace tree multipliers are a parallel digital multiplier architecture designed to minimize the worst-case time complexity of the circuit depth relative to the input size [1]. In…
MORPH: PDE Foundation Models with Arbitrary Data Modality
Mahindra Singh Rautela, Alexander Most, Siddharth Mansingh +6
We introduce MORPH, a modality-agnostic, autoregressive foundation model for partial differential equations (PDEs). MORPH is built on a convolutional vision transformer backbone th…
Towards Reasoning for PDE Foundation Models: A Reward-Model-Driven Inference-Time-Scaling Algorithm
Siddharth Mansingh, James Amarel, Ragib Arnab +10
Partial Differential Equations (PDEs) are the bedrock for modern computational sciences and engineering, and inherently computationally expensive. While PDE foundation models have…
VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization
Ayan Biswas, Terece L. Turton, Nishath Rajiv Ranasinghe +7
We present VizGenie, a self-improving, agentic framework that advances scientific visualization through large language model (LLM) by orchestrating of a collection of domain-specif…
Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval
Alexander Most, Joseph Winjum, Ayan Biswas +4
Retrieval-Augmented Generation (RAG) has become a popular technique for enhancing the reliability and utility of Large Language Models (LLMs) by grounding responses in external doc…
LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study
Nishath Rajiv Ranasinghe, Shawn M. Jones, Michal Kucer +5
Large Language Models (LLMs) are increasingly being leveraged for generating and translating scientific computer codes by both domain-experts and non-domain experts. Fortran has se…
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation
Manish Bhattarai, Javier E. Santos, Shawn Jones +3
The advent of large language models (LLMs) has significantly advanced the field of code translation, enabling automated translation between programming languages. However, these mo…
Explainable AI Integrated Feature Engineering for Wildfire Prediction
Di Fan, Ayan Biswas, James Paul Ahrens
Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our resear…