Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text
Solving Partial Differential Equations (PDEs) is ubiquitous in science and engineering. Computational complexity and difficulty in writing numerical solvers has motivated the development of data-driven machine learning techniques to generate solutions quickly. The recent rise in popularity of Large Language Models (LLMs) has enabled easy integration of text in multimodal machine learning models, allowing easy integration of additional system information such as boundary conditions and governing equations through text. In this work, we explore using pretrained LLMs to integrate various amounts of known system information into PDE learning. Using FactFormer as our testing backbone, we add a multimodal block to fuse numerical and textual information. We compare sentence-level embeddings, word-level embeddings, and a standard tokenizer across 2D Heat, Burgers, Navier-Stokes, and Shallow-Water data sets. These challenging benchmarks show that pretrained LLMs are able to utilize text descriptions of system information and enable accurate prediction using only initial conditions.
Code (1)
Tasks
SentenceSimilar Papers 제목 키워드 기반
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
In computational social science (CSS), researchers analyze documents to explain social and political phenomena. In most scenarios, CSS researchers first obtain labels for documents and then explain labels using interpret…
Uncertainty QuantificationvalidSurrogate Fidelity: When Can Open LLMs Explain Closed Ones?
Mechanistic interpretability (MI) requires full access to model internals, yet the APIs for most widely deployed language models at best expose log-probabilities over output tokens. This creates a surrogate problem: when…
Binary ClassificationExplaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model
One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability. In this paper, we describe a lightweight and effective method to derive explanations f…
Atari GamesDecision MakingDeep Reinforcement Learningreinforcement-learning+1Interpretable Spectrum Transformation Attacks to Speaker Recognition
The success of adversarial attacks to speaker recognition is mainly in white-box scenarios. When applying the adversarial voices that are generated by attacking white-box surrogate models to black-box victim models, i.e.…
Speaker RecognitionLearning to Explain: Prototype-Based Surrogate Models for LLM Classification
Large language models (LLMs) have demonstrated impressive performance on natural language tasks, but their decision-making processes remain largely opaque. Existing explanation methods either suffer from limited faithful…
Decision MakingSentence