3 citations · 3 across the 7 of their papers we have counts for
8 papers
Non-Uniform Quantum Fourier Transform
Junaid Aftab, Yuehaw Khoo, Haizhao Yang
The Discrete Fourier Transform (DFT) is central to the analysis of uniformly sampled signals, yet many practical applications involve non-uniform sampling, requiring the Non-Unifor…
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing
Jianda Du, Youran Sun, Haizhao Yang
PDEs are central to scientific and engineering modeling, yet designing accurate numerical solvers typically requires substantial mathematical expertise and manual tuning. Recent ne…
FMint-SDE: A Multimodal Foundation Model for Accelerating Numerical Simulation of SDEs via Error Correction
Jiaxin Yuan, Haizhao Yang, Maria Cameron
Fast and accurate simulation of dynamical systems is a fundamental challenge across scientific and engineering domains. Traditional numerical integrators often face a trade-off bet…
Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
Krishna Vamshi Bodla, Haizhao Yang
Since the introduction of Large Language Models (LLMs), they have been widely adopted for various tasks such as text summarization, question answering, speech-to-text translation,…
Parsing the Language of Expression: Enhancing Symbolic Regression with Domain-Aware Symbolic Priors
Sikai Huang, Yixin Berry Wen, Tara Adusumilli +2
Symbolic regression is essential for deriving interpretable expressions that elucidate complex phenomena by exposing the underlying mathematical and physical relationships in data.…
Curse of Dimensionality in Neural Network Optimization
Sanghoon Na, Haizhao Yang
This paper demonstrates that when a shallow neural network with a Lipschitz continuous activation function is trained using either empirical or population risk to approximate a tar…
Optimal Neural Network Approximation for High-Dimensional Continuous Functions
Ayan Maiti, Michelle Michelle, Haizhao Yang
Recently, the authors of \cite{SYZ22} developed a neural network with width and depth , which utilizes a special activation function called the elementary univers…
Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces
Yahong Yang, Yue Wu, Haizhao Yang +1
This paper introduces deep super ReLU networks (DSRNs) as a method for approximating functions in Sobolev spaces measured by Sobolev norms for with $m\ge…