Date/Time : 15 Apr 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Zhang Junyi, Nanyang Technological University

Quantum Approximate Optimization Algorithm (QAOA) was proposed as a variational quantum algorithm for solving combinatorial optimization problems. In this talk, I will discuss some recent observations in quantum circuit dynamics of QAOA. We identify three dynamical phases in the quantum circuits: 1) quasi-adiabatic phase, 2) quantum chaotic phase, and 3) shallow circuit phase. Particularly, we find that the quasi-adiabatic phase and the shallow circuit phase are separated by a dynamical quantum critical point indicated by the entanglement entropy. Moreover, the quantum chaotic phase emerges in digitized quantum circuit while absent for continuous unitary evolution. Our study calls for better understanding of quantum algorithms realized in quantum circuits.

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Date/Time : 08 Apr 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Feng Yue, Xi'an Jiaotong University

In this talk, I will introduce the long-time problem for the nonlinear Schrodinger equation (NLSE) with weak nonlinearity, which is characterized by with a dimensionless parameter. We solve the NLSE using various temporal discretization, including finite difference methods, exponential integrators and time-splitting methods combined with different spatial discretization. By introducing a new technique–Regularity Compensation Oscillation (RCO) which controls the high frequency modes by the regularity of the exact solution and analyzes the low frequency modes by phase cancellation and energy method, we carry out the improved uniform error bounds for the time-splitting Fourier pseudospectral method in the long-time regime. In addition, we design new exponential integrators for the low-regularity initial data and extend this result to other dispersive PDEs.

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Date/Time : 23 Feb 2026 16:30
Venue : S17 #04-04 (Seminar Room 3)
Speaker : Nils Thuerey, Technical University of Munich

In this talk, I will present recent advances from the area of AI and deep learning for physics simulations. A key focus is the utilization of numerical solvers capable of providing gradient information, i.e. “differentiable simulators”. These solvers seamlessly integrate with deep learning algorithms, presenting numerous advantages in arising from AI-based components in solvers, particularly in the context of flow simulations. However, the regular, first-order quantities like gradients are not necessarily optimal for optimizations and learning tasks. I will outline alternative updates, specifically those arising from modified back propagation paths, and discuss their advantages and disadvantages. In addition, I will give an outlook towards generative modeling for PDEs.

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Date/Time : 27 Jan 2026 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Ivan Au Yeung , Nvidia

This talk explores solving nonlinear p-Laplacian problems using NVIDIA PhysicsNeMo, a GPU-accelerated framework for building and training physics AI models at scale. The p-Laplacian is motivated through applications such as nonlinear diffusion and variational problems, with emphasis on the numerical difficulties that arise for large values of p and in complex 2D and 3D geometries. The core of the talk is a PhysicsNeMo-based workflow that recasts the p-Laplacian into Physics-Informed Neural Network and operator-learning formulations, enabling end-to-end training directly from PDE specifications and boundary conditions while leveraging multi-GPU hardware. Numerical experiments compare these neural solvers with traditional mesh-based methods in terms of accuracy, robustness for high p. The talk concludes with practical guidelines for setting up p-Laplacian problems and tuning architectures and loss terms within PhysicsNeMo.

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Date/Time : 21 Jan 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Kim Jeongho, Kyung Hee University

In this talk, I will present two different topics on numerical methods for solving PDEs.

In the first part, I will focus on the numerical methods for two-dimensional quantum mechanical systems interacting with electromagnetic fields. Precisely, we will discuss finite difference time domain method and time splitting Fourier Pseudospectral methods for Chern-Simons-Schrodinger/Dirac equations. In the second part, I will move on to the adaptive finite difference method for solving the Poisson equation. I will introduce the quad/octree structure on which the Poisson equation is solved. This provides a stable Hodge decomposition step in the projection method for incompressible flow.

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Date/Time : 14 Jan 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Li Xiaoli, Shandong University

The magnetization structures in the form of topological solitons have become important research interest following the fabrication of various ferromagnetic materials. In this talk we first present new higher-order implicit-explicit (IMEX) schemes for the LLG equation. These schemes are linear, length preserving, modified energy stable and only require solving one elliptic equation at each time step. We show that numerical solutions of these schemes are uniformly bounded without any restriction on the time step size, and establish rigorous error estimates of orders 1 to 5 in a unified framework. Next we present some recent developments in constructing the linear, length preserving and original energy preserving schemes.

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Date/Time : 09 Jan 2026 16:30
Venue : S17 #04-05 (Seminar Room 2)
Speaker : Kuang Yang, Guangdong University of Technology

Complex Langevin (CL) is a computational method to circumvent the numerical sign problem with applications in finite-density quantum chromodynamics and the real-time dynamics of quantum field theories. It has long been known that, depending on the simulated system, CL does not always converge correctly. We study the regularization of the complex Langevin method via augmenting the action with a stabilization term. We also show that, in certain situations, this regularization can introduce biases, and we propose several extensions to mitigate these effects and further enhance accuracy. Several numerical experiments in the lattice field theory are carried out to show the effectiveness of our approaches.

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Date/Time : 12 Dec 2025 15:00
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Su Chunmei, Tsinghua University

The quality of the mesh is crucial for simulating curvature flows, as standard approaches may fail due to mesh distortion. We first present a series of high-order parametric finite element methods based on the BGN formulation for solving various types of flows involving curves and surfaces. Extensive numerical experiments demonstrate the anticipated high-order accuracy while maintaining favorable mesh quality throughout the evolution process. Secondly, for flows involving multiple geometric structures, such as surface diffusion—which reduces area while preserving volume—we propose a type of structure-preserving method that incorporates two scalar Lagrange multipliers along with two evolution equations related to area and volume, respectively. These schemes effectively preserve the geometric structure at a fully discrete level. Comprehensive numerical experiments illustrate that our methods achieve the desired temporal accuracy, while simultaneously preserving the geometric structure of the surface diffusion.

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Date/Time : 29 Oct 2025 16:30
Venue : S17 #06-11 (Seminar Room 6)
Speaker : Guo Ling, Shanghai Normal University

Neural networks (NNs) are transforming computational approaches in physics and engineering by enabling new ways to integrate data with mathematical laws. This progress allows us to tackle inverse problems and ill-posed systems that are otherwise intractable using traditional methods. Accurately estimating predictive uncertainty is essential for using these models in complex scientific applications, yet remains a major challenge. In this talk, I will introduce a new framework for uncertainty quantification based on the information bottleneck principle (IB-UQ), designed for scientific machine learning tasks such as deep neural regression and neural operator learning. The framework allows for principled control over the information flow from inputs to outputs, resulting in improved uncertainty estimation. Furthermore, I will present a physics-informed extension of IB-UQ, tailored for solving PDE-related problems with noisy or partial observations. The effectiveness of this approach is demonstrated through several numerical examples, highlighting its potential in both data-driven and physics-constrained settings.

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