Introduction to Computational Quantum Physics

Speaker: Nan Sheng (Uchicago) Dates: 2023/04/21 Abstract: Quantum many-body physics is principally a problem where algorithmic complexity increases exponentially w.r.t. the system size. In order to tackle the quantum information encoded in the exponential scaling, robust computational methods are required….

Transformer meets boundary value inverse problems

Speaker: Ruchi Guo (UCI) Dates: 2023/04/07 Zoom link: click this link Abstract: A Transformer-based deep direct sampling method is proposed for solving a class of boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator…

Reduced order model for kinetic and transport problems

Speaker: Zhichao Peng (MSU) Dates: 2023/02/24 Zoom link: click this link Abstract: Numerical simulation plays an important role in various engineering and scientific problems. Reduced order model (ROM), a technique to reduce degrees of freedom needed in numerical simulations, is…

Deep Neural Network Modeling of Unknown System Dynamics

Speaker: Zhen Chen (Dartmouth) Dates: 2023/02/17 Zoom link: click this link Abstract: There are numerous observational, experimental, or simulation data for problems in science and engineering. Differential equations governing the underlying dynamics are often unknown for some systems. The ability…

Dynamical System Learning via Neural Networks

Speaker: Zhongshu Xu (OSU) Dates: 2023/02/09 Abstract: This talk will briefly review Prof Dongbin Xiu’s work on dynamical system learning and Neural Networks. First, I will discuss the connection between Euler forward scheme and ResNet, and why multi-step ResNet can…

Introduction to Bayesian Neural Networks.

Speaker: Tatsuoka Caroline (OSU) Dates: 2023/02/02 Abstract: I will be reviewing the Bayesian Neural Network framework. With sufficient data, current deep learning technologies offer a tool to better understand and predict dynamical systems behavior. However, uncertainty in predictions can arise…

Sketch-and-solve approaches to k-means clustering by semidefinite programming

Speaker: Kaiying Xie (OSU) Dates: 2023/01/27 Abstract: We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means optimal clustering. Otherwise, our approach provides a high-confidence…

Monotone meshfree methods for linear elliptic equations in non-divergence form via nonlocal relaxation

Speaker: Qihao Ye (UCSD) Dates: 2023/01/20 Zoom link: click this link Abstract: We design a monotone meshfree finite difference method for linear elliptic equations in the non-divergence form on point clouds via a nonlocal relaxation method. The key idea is…