1 citations · 1 across the 11 of their papers we have counts for
13 papers
DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation
Yizhou Zhang, Panqi Chen, Lei Cheng +4
Physics sensing in evolving environments requires reconstructing dense spatiotemporal fields from sparse observations while adapting where future observations should be collected.…
HarmoCore: Functional Latent Diffusion for Sparse Reconstruction of Oscillatory Wave Fields
Lihao Chen, Xinyu Zhang, Panqi Chen +4
Reconstructing oscillatory wave fields from scattered sensors is a severely underdetermined inverse problem. Beyond the challenges of general physical-field reconstruction, wave re…
TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing
Xinyu Zhang, Lihao Chen, Panqi Chen +4
Reconstructing continuous physical fields from sparse measurements is central to scientific monitoring, inverse modeling, and digital-twin construction. Generative reconstruction h…
Mitigating Spectral Bias in Neural Operators for Underwater Transmission Loss Prediction
Yifan Sun, Shikai Fang, Chao Zhang +3
Predicting underwater acoustic transmission loss rapidly and accurately is crucial for real-time ocean acoustic applications. While Fourier Neural Operators (FNO) have emerged as p…
Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation
Jiawei Chen, Xiaofan Gui, Shikai Fang +4
Parameterizing high-fidelity "digital twins" of batteries is a critical yet challenging inverse problem that hinders the pace of battery innovation. Prevailing methods formulate th…
APEX: Amplitude Anchors and Phase Priors for Target-Scarce Higher-Frequency Wave Prediction
Yifan Sun, Lei Cheng, Sijie Chen +3
Learning-based surrogates have become increasingly effective for wave-field prediction, and neural operators in particular have shown strong performance within observed frequency r…
StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models
Panqi Chen, Yifan Sun, Shikai Fang +2
Inferring the evolution of high-dimensional and multi-modal (e.g., spatio-temporal) physical fields from irregular sparse measurements in real time is a fundamental challenge in sc…
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
Yifei Zhang, Xu Yang, Xiao Yang +8
LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scalar validation scores to rank candidate…
When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning
Siyuan Li, Shikai Fang, Lei Cheng +4
Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitatio…
R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Xu Yang, Xiao Yang, Shikai Fang +13
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alle…
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
Panqi Chen, Yifan Sun, Lei Cheng +6
Modeling and reconstructing multidimensional physical dynamics from sparse and off-grid observations presents a fundamental challenge in scientific research. Recently, diffusion-ba…
Functional Complexity-adaptive Temporal Tensor Decomposition
Panqi Chen, Lei Cheng, Jianlong Li +4
Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal te…
MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
Junjie Li, Yang Liu, Weiqing Liu +4
Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite significant efforts to build real-…