9 papers
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…
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…
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…
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…
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…