Announcement Detail
Friday, October 24, 2025
10:00 AM EDT
Join via Zoom: https://utah.zoom.us/j/84332086970
Novel Method Virtual Seminar
Computational Modeling of Human Brain Neurodegeneration
Speaker
Paola F. Antonietti, MOX - Laboratory for Modeling and Scientific Computing
Abstract:
Neurodegenerative diseases (NDs), such as Alzheimer's and Parkinson's, are characterized by progressive functional impairment and structural brain deterioration. A common pathological hallmark is the accumulation and spreading of disease-specific misfolded and aggregated proteins. Despite extensive research, the mechanistic understanding of how these pathologies evolve remains an open field of study. This talk presents a hybrid physics-based and data-driven modeling framework aimed at better understanding key processes in neurodegeneration. First, we discuss the dynamics of misfolded protein aggregation and spread, utilizing multiscale mathematical models combined with machine learning-enhanced numerical discretization techniques to improve simulation accuracy and efficiency. In the second part, we focus on modeling the brain's waste clearance pathways, which are recognized as critical in the onset and progression of NDs. We also address the role of epileptiform activity in neurodegeneration by modeling seizure dynamics and their interactions with pathological protein accumulation.
Simulations are obtained on real brain geometries reconstructed from patient-specific clinical imaging data, allowing personalized computational models to better understand the interplay between structural, physiological, and pathological factors in neurodegenerative disease progression.
Bio:
Professor Paola F. Antonietti is Head of the Laboratory of Modeling and Scientific Computing MOX and Full Professor of Numerical Analysis at Politecnico di Milano. Her research centers on advanced (polytopal) numerical methods and computational learning techniques for the approximate solution of partial differential equations, with applications across various fields including computational neuroscience, engineering seismology, and subsurface flow simulations. Paola Antonietti has authored two books and over one hundred publications in international journals. She actively participates in many national and international research initiatives, as well as scientific advisory and editorial boards. In recognition of her significant contributions to applied mathematics and computational science, she received the 2016 SIMAI prize from the Italian Society of Applied and Industrial Mathematics and the 2020 Jacques-Louis Lions Award from ECCOMAS—the European Community on Computational Methods in Applied Sciences. She is the recipient of a 2023 ERC Synergy Grant, funded by the European Union.
Interpretable data-driven model discovery with global optimization: dynamical systems, ROMs, and operators
Speaker
Amirhossein Arzani, University of Utah
Abstract:
Lack of interpretability and generalization are among the key challenges in applying deep learning to physics-based systems. In this talk, we leverage some of the building blocks of neural networks, such as ADAM optimization and the PyTorch language, to discover dynamical systems models, interpretable nonlinear reduced-order models (ROMs) for spatiotemporal fluid flow, and interpretable latent spaces with operator learning. I first introduce ADAM-SINDy, a sparse identification framework that uses ADAM optimization for data-driven discovery of nonlinear dynamical systems. Unlike traditional sparse identification of nonlinear dynamics (SINDy), which often depends on prior knowledge of nonlinear parameters, ADAM-SINDy efficiently and accurately identifies them through a flexible global optimization scheme. I discuss how the sparse regression optimization task could be modified to achieve machine-precision accuracy. Multiple examples, including chaotic fluid flow and multiscale cancer systems biology, will be presented.
Building on this foundation, we introduce Decomposed Sparse Modal Optimization (DESMO) as an interpretable nonlinear ROM for spatiotemporal fluid flow data. Our method enhances proper orthogonal decomposition (POD) with nonlinear, data-driven corrections identified through ADAM optimization. We utilize unsteady fluid flow data to show that our approach can reduce the number of modes required for representing unsteady flows while maintaining interpretability and accuracy. Finally, I will present preliminary work demonstrating how similar ideas could be utilized in the context of operator learning and differentiable latent space model discovery.
Bio:
Dr. Amirhossein (Amir) Arzani is a tenured Associate Professor at the University of Utah (Scientific Computing and Imaging Institute and Mechanical Engineering Department). He obtained his BSc, MSc, and PhD degrees in mechanical engineering from Isfahan University of Technology, Illinois Institute of Technology, and UC Berkeley, respectively. He is the director of the Computational Biomechanics Group at Utah (https://bio.mech.utah.edu/) and a recipient of the NSF CAREER and NIH Trailblazer awards. Recently, he received the prestigious Presidential Early Career Award for Scientists and Engineers (PECASE) from President Biden. His research develops various computational mechanics and data-driven techniques for different applications, with a particular focus on biomedical flows and biomechanics.