3 citations · 4 across the 6 of their papers we have counts for
6 papers
Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization
Hao Xu, Siyu Lou, Yuntian Chen +1
Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate o…
Data-driven discovery of governing differential equations across physical systems
Siyu Lou, Hao Xu, Wenguan Wang +6
Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising al…
Cross-LLM Consistency in Inference: Evidence from Shared Interactions
Siyu Lou, Yao Yan, Yuntian Chen +1
Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns. In this paper, we e…
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs
Siyu Lou, Yuntian Chen, Xiaodan Liang +2
In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language g…
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression
Siyu Lou, Chengchun Liu, Yuntian Chen +1
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those d…
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
Mengge Du, Yuntian Chen, Longfeng Nie +2
Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate can…