Wednesday 10:00 -10:50 am, https://ucr.zoom.us/j/97606227247
Organizers : Weitao Chen / Heyrim Cho / Yat Tin Chow / Qixuan Wang / Jia Gou / Mykhailo Potomkin / Yiwei Wang / Maziar Raissi / Siting Liu
Past Organizers : Mark Alber / James Kelliher / Amir Moradifam
Archives: Fall 2019 / Winter 2020 / Spring 2020 / Fall 2020 / Winter 2021 / Spring 2021 / Fall 2021 / Winter 2022 / Spring 2022 / Fall 2022 / Winter 2023 / Spring 2023 / Fall 2023 / Winter 2024 / Spring 2024 /Fall 2024 / Winter 2025
Links : Interdisciplinary Center for Quantitative Modeling in Biology (ICQMB) seminar
In Spring 2025, the PDE & Applied Math seminar will be held through Zoom or in person (Skye 268). Specific information about the format of each talk will be provided in the email announcement and posted below. If you're interested in attending the seminar, please contact Dr. Jia Gou (jgou@ucr.edu) and Dr. Mykhailo Potomkin (mykhailp@ucr.edu).
Spring 2025 Schedule
Apr 9 10:00 (Wed) Organizational
Apr 16 10:00 (Wed) No seminar
Apr 23 10:00 (Wed) Dr. Mingyu Cai (UC Riverside)
Apr 30 10:00 (Wed) Dr. Ramin Bostanabad (UC Irvine)
May 7 10:00 (Wed) Dr. Xianjin Yang (California Institute of Technology)
May 14 10:00 (Wed) Dr. Stewart Mallory (The Pennsylvania State University)
May 21 10:00 (Wed) Dr. Trevor Leslie (Illinois Institute of Technology)
May 28 10:00 (Wed) Dr. Aparna Chandramowlishwaran (UC Irvine)
June 4 10:00 (Wed) Dr. Yongchao Huang (University of Aberdeen, UK)
Upcoming Talks:
June 04th, 2025, 10:00 AM - 11:00 AM PT
Dr. Yongchao Huang (University of Aberdeen, UK)
Title: Designing Particle-Based Samplers Using Physical Principles
Abstract: We introduce 3 new particle-based sampling methods inspired by physical principles: Electrostatics-based particle variational inference (EParVI), Smoothed Particle Hydrodynamics based particle variational inference (SPH-ParVI), and Material Point Method based particle variational inference (MPM-ParVI). Leveraging electrostatics, fluid dynamics, and continuum mechanics, these methods offer deterministic, flexible solutions for sampling complex, high-dimensional distributions in Bayesian inference and generative modeling. As exemplars of science for AI, these physics-based samplers pave the way for a new category of sampling techniques. This talk focuses on their mathematical formulations, algorithmic efficiency, and addresses some unresolved challenges.
Apr 23rd, 2025, 10:00 AM - 11:00 AM PT
Dr. Mingyu Cai (UC Riverside)
Title: Safe and Logic AI-enabled Autonomy
Abstract: Recent advances in machine learning enable the deployment of autonomous systems in our everyday life such as self-driving, smart home technologies, human-assistive robotics, etc. However, machine learning models may generate unexpected outputs that limit their success in practical applications. Real-world robotic deployments increasingly require algorithms to handle wider classes of complex tasks, while ensuring that these systems act safely. New fundamental questions are raised about building trustworthy AI-enabled autonomy. Researchers are working towards the next generation of robotics that can connect machine logic with natural languages, verify AI-based models, and assign interpretability to autonomous behaviors.
In this talk, I will introduce techniques from the field of formal methods, namely temporal logics, to complement a learning-enabled robot autonomy stack, thereby leading to safer and more interpretable robot behaviors. First, I will discuss how we can integrate formal logics to produce motion planning, machine learning, and control strategies to satisfy complex human-readable specifications. Second, I will demonstrate how formal logics can incorporate interpretable elements and tools into AI-enabled systems to provide safety-critical guarantees and verify neural network models. I will end the talk by showing existing open challenges and the deep potential of applying formal logics and machine learning for human-in-the-loop, perception, and generalization across robotic platforms, and thus the exciting directions these entail.
Bio: Mingyu Cai is currently an assistant professor in the Department of Mechanical Engineering at University of California, Riverside, where I am leading the Robotics and Explainable AI Lab (REAL). There, He is also an Advisor of the UCR Robotic Program as well as a cooperating faculty member in the Department of Electrical and Computer Engineering. His research specializes in robotics, machine learning, control, formal methods, aiming to enhance applications in autonomy systems, human-robot interaction, and AI Security.
Before joining UCR, He was a research scientist at Honda Research Institute (San Jose Office) from 2023-2024. From 2021-2023, he was a postdoctoral associate in the Autonomous and Intelligent Robotics Laboratory of Lehigh University and postdoc researcher of MIT Lincoln Lab. I received the Diploma in Mechanical Engineering in 2020 from the University of Iowa. He also received the M.Sc. and B.Eng in mechanical engineering from University of Florida, Gainesville, USA, 2017 and Beijing Institute of Technology, Beijing, China, 2015, respectively.
Apr 30th, 2025, 10:00 AM - 11:00 AM PT
Dr. Ramin Bostanabad (UC Irvine)
Title: Generalized Learning via Gaussian Processes
Abstract: Gaussian processes (GPs) are powerful machine learning models that have been successfully used in many applications that involve clustering, density estimation, system identification, regression, and classification. The majority of recent works on GPs are aimed at increasing their scalability to high dimensions and/or large datasets. In this talk, we will take a little detour from these works and focus on the fundamental limitations of kernels in the context of scientific machine learning. By addressing these limitations, we demonstrate that GPs can provide computationally efficient and competitive performance in critical applications ranging from resource allocation to topology optimization and operator learning.
May 07th, 2025, 10:00 AM - 11:00 AM PT
Dr. Xianjin Yang (California Institute of Technology)
Title: Data-Driven Methods for PDE Solutions and Model Discovery
Abstract: In this talk, I will present our recent advancements in solving partial differential equations (PDEs) and discovering models in complex systems. The first part focuses on our approaches for approximating PDE solutions with rigorous guarantees, addressing challenges such as rough and noisy forcing terms. This includes the development of novel loss functions, such as the Negative Sobolev Norm for Gaussian Process (GP) methods and physics-informed neural networks (PINNs), along with a sparse GP approach to enhance computational efficiency. The second part highlights our work on uncovering unknown parameters and dynamics in scientific systems, utilizing hypergraph-based models and GP frameworks.
May 14th, 2025, 10:00 AM - 11:00 AM PT
Dr. Stewart Mallory (The Pennsylvania State University)
Title: Phase behavior and transport of active colloids under extreme confinement
Abstract: Understanding transport in driven particle systems presents a fundamental challenge in nonequilibrium statistical mechanics, particularly under extreme geometric constraints. In this work, we study active colloidal suspensions confined to narrow channels, where particles cannot pass one another and are restricted to single-file motion. This setting offers a minimal model for exploring how confinement alters phase behavior and collective transport in active matter.
A key aspect of our study is the introduction of a kinetic temperature—a nonequilibrium measure of velocity fluctuations—which plays a central role in determining the mechanical equation of state and effective transport properties such as the mean squared displacement. Using a combination of Brownian dynamics simulations and coarse-grained modeling, we demonstrate how activity and confinement together reshape the relationship between microscopic forces and macroscopic observables.
We further show that the bulk equation of state can be inferred from sedimentation profiles, connecting steady-state density distributions to pressure in a system far from equilibrium. These results contribute to a growing theoretical framework for understanding confined transport in active systems, with implications for microfluidic devices, biological channels, and more broadly, the modeling of driven diffusive systems in narrow geometries.
May 21st, 2025, 10:00 AM - 11:00 AM PT
Dr. Trevor Leslie (Illinois Institute of Technology)
Title: Limiting configurations of solutions to the Euler Alignment system
Abstract: In the Euler Alignment system from collective dynamics, a typical long-time behavior is convergence of the density profile to a traveling wave solution (a "flocking state"). When one restricts attention to a single spatial dimension, it is often possible to describe features of the limiting density profile quite explicitly, in terms of the initial data and the communication protocol only. In this talk, we will provide an introduction to the Cucker-Smale and Euler Alignment systems and then discuss the flocking states in 1D. Most of the talk will be based on joint work with Changhui Tan.
May 28th, 2025, 10:00 AM - 11:00 AM PT
Dr. Aparna Chandramowlishwaran (UC Irvine)
Title: Learning Boiling Dynamics with Transformers: From Prediction to Forecasting
Abstract: Boiling is a chaotic, multiphase transport phenomenon central to thermal and energy systems, yet remains difficult to model due to the nonlinear coupling of turbulence, phase change, and nucleation. In this talk, I will present our recent efforts to model boiling dynamics using machine learning: BubbleML and Bubbleformer. BubbleML is a large-scale dataset built from high-fidelity multiphysics simulations, aimed at catalyzing progress in ML-driven modeling of multiphase flows. Bubbleformer is a transformer-based forecasting model trained on this dataset to predict the spatiotemporal evolution of boiling, including bubble nucleation, interface dynamics, and heat transfer--generalizing across fluids, geometries, and operating conditions. I will conclude, time permitting, with an overview of Mondrian, a framework for transformer-based neural operators inspired by domain decomposition and designed for resolution-invariant learning.