4 citations · 8 across the 13 of their papers we have counts for
6 papers · 1 filter
Self-Supervised Amortized Neural Operators for Optimal Control: Scaling Laws and Applications
Wuzhe Xu, Jiequn Han, Rongjie Lai
Optimal control provides a principled framework for transforming dynamical system models into intelligent decision-making, yet classical computational approaches are often too expe…
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
Chirag Modi, Jiequn Han, Eric Vanden-Eijnden +1
Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean dat…
ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
Haolan Zheng, Yanlai Chen, Jiequn Han +1
We propose a novel data-lean operator learning algorithm, the Reduced Basis Neural Operator (ReBaNO), to solve a group of PDEs with multiple distinct inputs. Inspired by the Reduce…
DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
Yinuo Ren, Wenhao Gao, Lexing Ying +2
We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods…
Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Jiequn Han, Kui Ren, Nathan Soedjak
We propose an instance-wise adaptive sampling framework for constructing compact and informative training datasets for supervised learning of inverse problem solutions. Typical lea…
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
Rudy Morel, Jiequn Han, Edouard Oyallon
We address the problem of predicting the next state of a dynamical system governed by unknown temporal partial differential equations (PDEs) using only a short trajectory. While st…