Past Events

Seminars are held Tuesdays and Thursdays in POB 6.304 from 3:30-5:00 pm, unless otherwise noted. Speakers include scientists, researchers, visiting scholars, potential faculty, and ICES/UT Faculty or staff. Everyone is welcome to attend. Refreshments are served at 3:15 pm.

Friday, Nov 15

A Machine Learning Model for Heart Valve Remodeling

Friday, Nov 15, 2019 from 1PM to 2PM | POB 4.304

  • Additional Information

    Hosted by Michael Sacks

    Sponsor: Oden Seminar-WCCMS Series

    Speaker: Wenbo Zhang

    Speaker Affiliation: CSEM Ph.D. student, Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for CSE, UT Austin

  • Abstract

    Predictive structural constitutive models have been developed for time-evolving properties of exogenously cross-linked collagenous soft tissues under cyclic loading for remodeling of bioprosthetic heart valves. To simulate the long-term responses of novel heart valve designs, efficient constitutive models are crucial. To this end, we build a neural network model with improved efficiency that can replicate the responses of structural models for soft tissues with a range of fiber structures. The neural network constitutive model is integrated with high order structural tensors for mapping fiber structure, allowing patient-specific simulation.

    Bio
    Wenbo Zhang is a CSEM Ph.D. student in the Willerson Center for Cardiovascular Modeling and Simulation, supervised by Dr. Michael Sacks.


Friday, Nov 15

  • Additional Information

    Hosted by Tom O'Leary-Roseberry

    Sponsor: Oden Seminar-Babuska Forum Series

    Speaker: Karen Willcox

    Speaker Affiliation: Professor, AE/EM, and Director - Oden Institute for Computational Engineering and Sciences, UT Austin

  • Abstract

    Achieving predictive data science for physical systems requires a synergistic combination of data and physics-based models, as well as a critical need to quantify uncertainties. For many frontier science and engineering challenge problems, a purely data-focused perspective will fall short---these problems are characterized by complex multi-scale multi-physics dynamics, high-dimensional uncertain parameters that cannot be observed directly, and a need to issue predictions that go beyond the specific conditions where data may be available. Learning from data through the lens of models is a way to bring structure to an otherwise intractable problem: it is a way to respect physical constraints, to embed domain knowledge, to bring interpretability to results, and to endow the resulting predictions with quantified uncertainties. This talk highlights how physics-based models and data together unlock predictive modeling approaches through the example of building a Digital Twin for structural health management of an unmanned aerial vehicle.

    Bio
    Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences and a Professor of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. She holds the W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences and the Peter O'Donnell, Jr. Centennial Chair in Computing Systems. Prior to joining the Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as the founding Co-Director of the MIT Center for Computational Engineering and the Associate Head of the MIT Department of Aeronautics and Astronautics. Prior to joining the MIT faculty, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. Her research at MIT has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on model reduction as a way to learn principled approximations from data and on multi-fidelity formulations to leverage multiple sources of uncertain information. She is a Fellow of SIAM and Associate Fellow of AIAA.


Friday, Nov 15

Blitz presentatons. See abstract for titles

Friday, Nov 15, 2019 from 11AM to 12PM | POB 6.304

  • Additional Information

    Hosted by Bryan Reuter

    Sponsor: Oden Seminar-Student Forum Series

    Speaker: Hieu Nguyen, Tim Smith, and Max Bremer

    Speaker Affiliation: Oden Institute, The University of Texas at Austin

  • Abstract

    The topics will include (in order of the speakers):
    Some Attempts Using CNN to Enhance Solution of Linear Wave Equation; xarray: the tool that converted me to python; Overview of Extreme Heterogeneit


Thursday, Nov 14

Understanding signal propagation in nicotinic acetylcholine receptors

Thursday, Nov 14, 2019 from 2PM to 3:30PM | POB 6.304

  • Additional Information

    Hosted by Ron Elber

    Sponsor: Oden Seminar-Molecular Biophysics Series

    Speaker: Sofia Oliveira

    Speaker Affiliation: School of Biochemistry and Centre for Computational Chemistry, University of Bristol

  • Abstract

    Cigarette smoking is considered, nowadays, to be a significant public health problem. Recent estimates indicate that approximately 1/4th of the world's population smokes1 and that smoking is the second most prevalent cause of death in the world2. Currently, the FDA-approved smoking cessation drugs, such as varenicline, are only moderately effective in reducing the symptoms of nicotine withdrawal and may cause undesirable side effects. Consequently, there is a growing need to develop new smoking cessation agents with improved effectiveness and tolerability.
    Nicotine is the major biologically psychoactive agent in tobacco, and it binds to the nicotinic acetylcholine receptors (nAChRs)3. These receptors mediate synaptic transmission in the nervous system and are therapeutic targets for various neurodegenerative diseases, psychiatric and neurodevelopmental disorders, including nicotine addiction3. Over the last decades, nAChRs have been widely explored, and our understanding of their molecular mechanisms has made extensive progress. However, despite a plethora of available structural and biochemical data, it is still not clear how ligand binding induces the conformational changes necessary to modulate the receptor’s dynamics. Answering this question requires knowledge of the dynamics of the protein and the identification of the conformational changes that take place upon ligand binding. Molecular dynamics (MD) simulations offer a highly effective method to identify, ‘assay’ and analyse functionally important motions of proteins and recently we have used a combination of equilibrium and nonequilibrium molecular dynamics simulations to map dynamic and structural changes induced by nicotine in two of the most relevant nAChRs, namely the α4β24,5 and the α74,6 subtypes. Our simulations reveal a striking pattern of communication between the agonist-binding pockets and the transmembrane domains and show the sequence of conformational changes associated with the initial steps of signal propagation.


  • Additional Information

    Hosted by Tom O'Leary-Roseberry

    Sponsor: Oden Seminar-Babuska Forum Series

    Speaker: Leszek Demkowicz

    Speaker Affiliation: Oden Institute for Computational Engineering and Sciences, UT Austin

  • Abstract

    In the mixed formulation of the ideal Petrov-Galerkin method with optimal test functions, one solves for an approximate solution coming from a discrete trial space, along with the Riesz representation of the corresponding residual coming from the exact test space. The residual provides an ideal a-posteriori error estimate providing a basis for adaptive refinements of trial space. This is the outer adaptivity loop.

    To arrive at a practical method, we need to approximate somehow the residual. In the standard DPG method this is done by employing a sufficiently large discrete (enriched) test subspace of the test space. For (benign) single scale problems, this can be done by implementing elements of higher (enriched) order and constructing appropriate Fortin operators to assess the lost of stability due to the approximation of the exact residual. The situation is quite different for singular perturbation problems where one strives for the robustness, i.e. uniform stability with respect to the perturbation parameter.

    Alternatively, with the given approximate trial space, one can solve for the approximate residual ADAPTIVELY. This is the inner adaptivity loop. For singular perturbation problems the challenge comes from the need for a ROBUST a-posteriori error estimation technique.

    We propose an inner adaptivity loop built upon the classical duality theory and a-posteriori error estimation based on duality gap estimate (the classical hypercircle methodology). The methodology will be illustrated with a convection-dominated diffusion (``confusion'') problem.

    The double adaptivity algorithm delivers solutions for the diffusion constant epsilon = 10^-7 in a fully automatic mode. The adapted trial meshes with the corresponding adaptively obtained test meshes do NOT satisfy the robust inf-sup condition.


  • Additional Information

    Hosted by Michael Sacks

    Sponsor: Oden Seminar-WCCMS series

    Speaker: Reza Avaz

    Speaker Affiliation: Research scientist, Computational cardiac modeling, Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for CS&E at UT Austin.

  • Abstract

    The development of integrated computational-experimental models of normal and impaired hearts offers novel ways to better understand the pathophysiology of heart remodeling in response to structural heart diseases and to design and personalize cardiac interventions. Pulmonary arterial hypertension (PAH) is a progressive structural heart disease that imposes a chronic pressure overload in the right ventricle (RV), leading to substantial remodeling events including hypertrophy of muscle cells and dilation of the RV. Many studies, including ours, suggest that the fate of a patient with PAH is not determined by the degree of pressure overload but rather by how the RV responds to it. However, the question of how to predict whether the RV remodeling in response to PAH stabilizes or rapidly transitions to RV failure remains largely unanswered.

    In this talk, I will present our work on developing rat-specific computational heart models to study time-course cardiac remodeling in response to PAH. I will discuss novel insights, from our model, delineating how the remodeling events at multiple scales in the myocardium (including cellular, fiber, and tissue levels) collectively lead to the cardiac function impairment in the RV. In particular, I aim to demonstrate the capability of our model to identify predictive biomarkers for early diagnosis of PAH, and to guide subsequent patient-specific pharmaceutical and medical device interventions. Along these lines, I will conclude my talk by discussing how our integrated modeling platform offers an effective approach for the optimal design of ventricular assist devices, and serves as an important step towards identifying and evaluating therapeutic targets for drug development to treat PAH.

    Bio
    Dr. Reza Avaz is a research scientist in the area of computational cardiac modeling in the Willerson Center for Cardiovascular Modeling and Simulation at the University of Texas at Austin. Dr. Avaz earned his PhD in Mechanical Engineering from the University of Pennsylvania (2014) in the area of constitutive modeling of soft materials. Dr. Avaz’s research interests are focused on developing and using multiphysics integrated computational-experimental models of the heart under normal, pathological and therapeutic conditions for the design and optimization of regenerative therapies to treat structural heart diseases.


Thursday, Nov 7

  • Additional Information

    Hosted by Tan Bui-Thanh

    Sponsor: Oden Seminar

    Speaker: Dmitri Kuzmin

    Speaker Affiliation: Professor, Technical University of Dortmund, Germany

  • Abstract

    In this talk, we review some recent advances in the analysis and design of algebraic flux correction (AFC) schemes for hyperbolic problems. In contrast to most variational stabilization techniques, AFC approaches modify the standard Galerkin discretization in a way which provably guarantees the validity of discrete maximum principles for scalar conservation laws and invariant domain preservation for hyperbolic systems. The corresponding inequality constraints are enforced by adding diffusive fluxes, and bound-preserving antidiffusive corrections are performed to obtain nonlinear high-order approximations. After introducing the AFC methodology and the underlying theoretical framework in the context of continuous piecewise-linear finite element discretizations, we present some of the limiting techniques that we use in high-resolution AFC schemes. As an alternative to flux-corrected transport (FCT) algorithms which apply limited antidiffusive corrections to bound-preserving low-order solutions, we propose a new limiting strategy based on representation of these solutions as convex combinations of "bar states" satisfying physical and numerical admissibility conditions. Each antidiffusive flux is limited so as to guarantee that the associated bar state remains in the convex invariant set and preserves appropriate local bounds. There is no free parameter, and the nonlinear discrete problem is well-defined even in the steady-state limit. In the case study for the Euler equations of gas dynamics, we enforce local maximum principles for the density, velocity, and specific total energy in addition to positivity preservation for the density and pressure. The results of numerical studies for standard test problems illustrate the ability of the methods under investigation to resolve steep gradients without generating spurious oscillations. In the last part of this talk, we discuss the design of AFC schemes for high-order finite elements. The approaches to be explored are based on the use of Bernstein basis functions and partitioned finite element spaces.


Friday, Nov 1

Studies on the 3D mechanical properties of myocardium in health, disease, and treatment

Friday, Nov 1, 2019 from 1PM to 2PM | POB 4.304

Important Update: Note: Seminar Location is POB 4.304
  • Additional Information

    Hosted by Michael Sacks

    Sponsor: Oden Seminar-WCCMS Series

    Speaker: David Li

    Speaker Affiliation: Willerson Center for Cardiovascular Modeling and Simulation, UT Austin

  • Abstract

    Heart failure, one of the leading causes of death worldwide, results from adverse remodeling of the heart after myocardial infarction (MI). Infarction modification through direct injection of biomaterials to reinforce the tissue has the ability to attenuate the risk of developing heart failure. The design of optimal materials for injection and their deployment can be accomplished in-silico using computational models of the post-MI remodeling heart, allowing for the development of patient-specific therapies for MI, as well as improvement of patient outcomes.

    Bio
    David Li is a 5th year PhD student in the Willerson Center for Cardiovascular Modeling and Simulation, supervised by Dr. Michael Sacks.


  • Additional Information

    Hosted by Bryan Reuter

    Sponsor: Oden Seminar-Student Forum Series

    Speaker: 1.Shane McQuarrie, 2.Louis Ly, and 3.Sean McBane

    Speaker Affiliation: Oden Institute, UT Austin

  • Abstract

    There will be three short talks during the session.

    The topics will include (in order of the speakers):
    1. A Gentle Introduction to Model Order Reduction via Proper Orthogonal Decomposition
    2. Problems in Visibility - Learning Surveillance and Exploration
    3. Component-Wise Model Reduction for Lattice Optimization.

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Friday, Oct 25

Patient-specific modeling with applications to the cardiovascular and cerebrovascular system.

Friday, Oct 25, 2019 from 10AM to 11AM | POB 2.402 (Electronic)

Important Update: NOTE: Different Location (POB 2.402)
  • Additional Information

    Hosted by Kendrick Shepherd and Tom O'Leary-Roseberry

    Sponsor: Oden Seminar-Babuska Forum Series

    Speaker: Shaolie Hossain

    Speaker Affiliation: Department of Molecular Cardiology, Texas Heart Institute, Houston

  • Abstract

    Patient-specific modeling is a relatively new paradigm in medical planning that utilizes computational tools on anatomical and physiological data to individualize patient care. It has the potential to optimize surgical procedures and to improve diagnosis and treatment of a number of common illnesses. In pursuit of these ends, image-based patient-specific modeling techniques have been used to study various organs including the heart, the brain, bones, teeth, kidneys, tumors, and lungs. In this talk, I will go over the main aspects of patient specific vascular modeling and present a few clinically relevant applications.

    Bio:
    Dr. Hossain is an Associate Research Professor at the Oden Institute at the University of Texas at Austin and a Senior Research Scientist at the Texas Heart Institute (THI) in Houston. She spearheads a joint initiative between THI and the Oden Institute for developing simulation technologies that exploit advances in computational methods to further our understanding of vascular disease, and directly guide the conceptualization, development, and clinical evaluation of new therapies. Dr. Hossain is a mechanical engineer by training specializing in computational fluid dynamics, patient-specific modeling and nanoparticulate drug delivery. She received her bachelor’s degree from Bangladesh University of Engineering and Technology, master’s degree from Stanford University and Ph.D. degree from the University of Texas at Austin. Her research has been supported by the National Institute of Health, the National Science Foundation, William and Ella Owens Foundation, The Farish Stamp Foundation, UT-Austin|Portugal CoLab, and Abbott Vascular Inc.