6 citations · 12 across the 15 of their papers we have counts for
16 papers
Refresh or Realize? Compute Allocation in Drifting Models
Sipeng Chen, Xu Zheng, Shibo Li
Drifting Models train a one-step generator by recomputing a finite-sample drift field at every iteration and taking an optimizer step toward the drifted target. The field says how…
Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions
Sipeng Chen, Junliang Liu, Hewei Tang +1
The source distribution in conditional flow matching is a design parameter that can be calibrated to data, not a default isotropic prior. We exploit this in Multi-Fidelity Flow Mat…
Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
Yan Zhang, Xuefeng Liu, Sipeng Chen +3
Standard Bayesian Optimization (BO) assumes uniform smoothness across the search space an assumption violated in multi-regime problems such as molecular conformation search through…
Dynamic Bayesian Optimization Framework for Instruction Tuning in Partial Differential Equation Discovery
Junqi Qu, Yan Zhang, Shangqian Gao +1
Large Language Models (LLMs) show promise for equation discovery, yet their outputs are highly sensitive to prompt phrasing, a phenomenon we term instruction brittleness. Static pr…
Beyond Heuristics: Globally Optimal Configuration of Implicit Neural Representations
Sipeng Chen, Yan Zhang, Shibo Li
Implicit Neural Representations (INRs) have emerged as a transformative paradigm in signal processing and computer vision, excelling in tasks from image reconstruction to 3D shape…
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
Zhan Cheng, Bolin Shen, Tianming Sha +3
Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attack…
COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
Junqi Qu, Tao Wang, Yushun Dong +2
Multiphysics simulations play an essential role in accurately modeling complex interactions across diverse scientific and engineering domains Although neural operators especially t…
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation
Zheng Wang, Shibo Li, Shikai Fang +1
Multi-fidelity surrogate learning is important for physical simulation related applications in that it avoids running numerical solvers from scratch, which is known to be costly, a…
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
Shikai Fang, Xin Yu, Shibo Li +3
Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and…
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
Shikai Fang, Xin Yu, Zheng Wang +3
Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a…
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Shikai Fang, Madison Cooley, Da Long +3
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs). How…
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
Zheng Wang, Shikai Fang, Shibo Li +1
Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however…
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Shibo Li, Michael Penwarden, Yiming Xu +4
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply differen…
Infinite-Fidelity Coregionalization for Physical Simulation
Shibo Li, Zheng Wang, Robert M. Kirby +1
Multi-fidelity modeling and learning are important in physical simulation-related applications. It can leverage both low-fidelity and high-fidelity examples for training so as to r…
Meta-Learning with Adjoint Methods
Shibo Li, Zheng Wang, Akil Narayan +2
Model Agnostic Meta Learning (MAML) is widely used to find a good initialization for a family of tasks. Despite its success, a critical challenge in MAML is to calculate the gradie…
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Shibo Li, Robert M. Kirby, Shandian Zhe
Bayesian optimization (BO) is a powerful approach for optimizing black-box, expensive-to-evaluate functions. To enable a flexible trade-off between the cost and accuracy, many appl…