Date/Time : 19 Aug 2026 16:30
Venue : S17 #04-05 (Seminar Room 2)
Speaker : Liu Lei, Chongqing University

Nonlinear waves on nonzero asymptotic backgrounds, such as breathers and rogue waves, have been hot research topics in recent years. Starting with the three-wave resonant interactions system, we identify a specific rogue waves-building mechanism based on modulational instability (MI) which includes zero wavenumber in the gain band. An essential finding is that this mechanism works solely under a linear relation between the MI gain and a vanishingly small wavenumber of the modulational perturbation. We also report the emergence of a previously unexplored species of solitary waves-time-localized solitons, which coincides with the condition for the occurrence of the zero-wavenumber-gain MI. These results can be extended to the Massive Thirring model

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Date/Time : 04 Aug 2026 11:00
Venue : S17 #04-05 (Seminar Room 2)
Speaker : Timo Sprekeler, Texas A&M University

In this talk, we discuss a computational multiscale method for the mixed formulation of Darcy’s problem, based on a stable localized orthogonal decomposition in Raviart-Thomas finite element spaces. In the spirit of numerical homogenization, the construction provides low-dimensional coarse approximation spaces that incorporate fine-scale information from the heterogeneous coefficients by solving local patch problems on a fine mesh. The resulting numerical scheme is applicable beyond periodicity and scale-separation. This is joint work with Patrick Henning (Bochum) and Hao Li (Hong Kong).

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Date/Time : 29 Jul 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Yang Siyao, University of Chicago

High-dimensional probability distributions arise throughout scientific computing, yet their estimation, representation, and evaluation are often hindered by the curse of dimensionality. In this talk, I will present a tensor-train-based framework for learning high-dimensional densities from samples. The method first smooths the empirical distribution to reduce its large variance, compresses the resulting coefficient tensor using efficient tensor-train algorithms, and then applies a deconvolution step to recover a functional density estimator. The proposed method can achieve computational complexity linear in both the dimension and the number of samples. Its performance is demonstrated on several tasks, including high-dimensional Boltzmann distribution estimation and image generation.

I will then discuss an application of this framework to molecular dynamics called TT-Metadynamics. In this method, the growing sum of Gaussian bias potentials generated during metadynamics is periodically compressed into a functional tensor train. This avoids exponential grid storage and prevents the cost of evaluating the bias potential from increasing with simulation time. The method enables free-energy exploration with up to 14 collective variables and performs favorably compared with standard metadynamics in high-dimensional examples.

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Date/Time : 15 Jul 2026 14:00
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Wang Hao, Sichuan University

Solid-state phase transitions typically follow a Minimum Energy Path (MEP). Classical methods such as G-SSNEB compute the MEP in the full high-dimensional configuration space, which becomes prohibitive when paths must be scanned over pressures, defect types, and system sizes. We propose the Preset Structure Strain Path (PSSP) method, which uses feature extraction and transfer learning to compute MEPs efficiently across these conditions. PSSP is based on the fact that in defective systems, lattice strain sets the direction of the MEP, while the local atomistic environment changes little but governs the energy. PSSP fully exploits this separation by presetting the displacement field as a transferable feature and optimizing only the lattice evolution in a low-dimensional space. By doing this, the strain paths learned in one setting can be reused under different pressures, defects and system sizes. The advantage of PSSP is verified that it reduces the energy and force evaluations to less than 2% of G-SSNEB but preserving the accuracy of the energy barrier in a satisfactory level.

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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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