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DTSTAMP:20260604T161002
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SUMMARY:Frontiers in Scientific Machine Learning Seminar - Ashwin Renganathan (Penn State): Sample-efficient and Principled Decision-making  with Expensive Stochastic Oracles
DESCRIPTION:Zoom link \nBio: Ashwin Renganathan is an assistant professor of aerospace engineering at Penn State and holds a joint appointment with the Penn State Institute of Computational and Data Sciences (ICDS). He directs the Computational complex engineered Systems Design Laboratory (CSDL) at Penn State. He is broadly interested in developing novel and scalable computational techniques for surrogate modeling\, uncertainty quantification\, and numerical optimization\, with a focus on aerospace applications. He earned his Ph.D. in aerospace engineering from Georgia Tech and previously completed a postdoctoral appointment in applied mathematics at the Argonne National Laboratory. \nSample-efficient and Principled Decision-making with Expensive Stochastic Oracles\nAbstract: Modern day engineering decision-making involves one or more computer simulation oracles of an engineered system which can be queried on-demand to learn the system response to control input. Querying simulation oracles\, also called “computer experiments”\, incur a non-trivial computational cost\, which increases with the level of fidelity in the underlying models. For instance\, a realistic computational aerodynamic simulation of an aircraft can cost several thousands of CPU hours to compute—anything more than a few dozens of such simulations is prohibitive. Therefore\, a central goal of engineering decision-making is to optimally design computer experiments\, to maximize the value of information extracted at minimal computational effort.\nIn this talk\, we will address problems anchored in\, what we coin\, the “decision-making triad” which includes: surrogate modeling\, uncertainty quantification (UQ)\, and numerical optimization/control. Specifically\, using variants of a probabilistic surrogate model and a Bayesian decision theoretic framework\, we will show that problems in the decision-making triad can be solved in a principled\, theoretically sound and\, yet (computational) cost-effective manner. We will show demonstrations on applications in computational aerodynamics.
URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-seminar-15-ashwin-renganathan/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/MICDE-Seminar-Series-Speaker-Portraits-4.png
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DTSTART;TZID=America/Detroit:20250620T120000
DTEND;TZID=America/Detroit:20250620T130000
DTSTAMP:20260604T161002
CREATED:20250619T132221Z
LAST-MODIFIED:20250620T145718Z
UID:10000823-1750420800-1750424400@micde.umich.edu
SUMMARY:Frontiers in Scientific Machine Learning Seminar - Pan Du (University of Notre Dame): Conditional neural field latent diffusion model for generating spatiotemporal turbulence
DESCRIPTION:Zoom link \nBio: Pan Du received his bachelor’s degree in Thermal Engineering from Tsinghua University and completed his master’s in Mechanical Engineering at Washington University in St. Louis. He is currently a Ph.D. candidate in Aerospace and Mechanical Engineering at the University of Notre Dame under the guidance of Prof. Jian-Xun Wang. Pan’s research spans multiple disciplines\, including scientific machine learning\, Bayesian inference\, uncertainty quantification\, geometric deep learning\, and computational fluid mechanics. \nConditional neural field latent diffusion model for generating spatiotemporal turbulence\nAbstract: Pan Du will present the CoNFiLD model\, a novel generative framework for simulating complex turbulent flows in 3D irregular domains. While traditional eddy-resolved simulations are accurate\, their high computational cost limits usability. CoNFiLD addresses this by integrating neural field encoding with latent diffusion\, enabling efficient\, probabilistic modeling of spatiotemporal dynamics. It supports a wide range of tasks—such as flow super-resolution\, sparse reconstruction\, and data restoration—via Bayesian conditional sampling\, all without retraining. Results across diverse turbulent scenarios highlight its potential for advancing data-driven turbulence modeling.
URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-pan-du-university-of-notre-dame/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/Pan-Du-1.png
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DTSTART;TZID=America/Detroit:20250711T120000
DTEND;TZID=America/Detroit:20250711T130000
DTSTAMP:20260604T161002
CREATED:20250708T153951Z
LAST-MODIFIED:20250708T153951Z
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SUMMARY:Frontiers in Scientific Machine Learning Seminar - Romit Maulik (Penn State University): SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
DESCRIPTION:Zoom link \nBio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics\, geophysical modeling\, nuclear fusion\, and beyond. He is an Early Career Awardee of the Army Research Office. \nSALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning\nAbstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However\, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation\, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions)\, a novel RL framework that models control actions as dynamic\, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system\, our approach enables interpretability through local stability analysis\, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation\, SALSA-RL provides a powerful tool for advancing the design\, analysis\, and theoretical understanding of RL systems.
URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-romit-maulik-penn-state-university/
LOCATION:2004 Lay Auto Lab
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/07/Romit-Maulik-PennState-University.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250711T120000
DTEND;TZID=America/Detroit:20250711T130000
DTSTAMP:20260604T161002
CREATED:20250708T154831Z
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SUMMARY:Frontiers in Scientific Machine Learning Seminar 17: SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
DESCRIPTION:Date: June 6\, 2025\, 12pm – 1pm\nThis is a hybrid event. To join via Zoom: Meeting ID: 978 2352 7756\, Passcode: Enter last year in format YYYY\nTo join in person: 2004 Lay Auto Lab. Refreshments will be available! \nAbstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However\, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation\, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions)\, a novel RL framework that models control actions as dynamic\, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system\, our approach enables interpretability through local stability analysis\, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation\, SALSA-RL provides a powerful tool for advancing the design\, analysis\, and theoretical understanding of RL systems.\nBio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics\, geophysical modeling\, nuclear fusion\, and beyond. He is an Early Career Awardee of the Army Research Office.
URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-seminar-17-salsa-rl-stability-analysis-in-the-latent-space-of-actions-for-reinforcement-learning/
LOCATION:Walter E Lay Auto Lab – 2004
CATEGORIES:Deep Learning,FSML,Interdisciplinary,Machine Learning,North Campus,Scientific Computing,Sciml
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