27 papers
Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction
Jiaquan Zhang, Shuxu Chen, Haifan Meng +6
Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However, long-horizon autoregressive prediction remains chall…
HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators
Jiaquan Zhang, Shuxu Chen, Haifan Meng +6
Neural operators provide fast surrogates for time-dependent partial differential equations (PDEs) by applying a learned evolution operator recursively to its own predictions, but t…
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
Shuxu Chen, Yitian Zhou, Jiaquan Zhang +6
Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However, CoT reasoning can be unstabl…
TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations
Yitian Zhou, Chaoning Zhang, Zhenzhen Huang +8
Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution can drift over time. While…
Autoregression-Free Neural Operators for Time-Dependent PDEs
Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7
Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent…
Topology-Aware Layer Pruning for Large Vision-Language Models
Pengcheng Zheng, Chaoning Zhang, Ya Wen +10
Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, while recent extensions that incorporate visual inputs enable th…