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Conferences, Lectures, & Seminars
Events for December
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Dec 01, 2021 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mi Zhang , Machine Learning Systems Lab at Michigan State University
Talk Title: Empowering the Next Billion Devices with Deep Learning
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The proliferation of edge devices and the gigantic amount of data they generate make it no longer feasible to transmit all the data to the cloud for processing. Such constraints fuel the need to move the intelligence from the cloud to the edge where data reside. In this talk, I will present our works on how we bring the power of deep learning to edge devices to realize the vision of Artificial Intelligence of Things (AIoT).
First, I will present our work on designing adaptive frameworks that empower AI-embedded edge devices to adapt to the inherently dynamic runtime resources to enable elastic on-device AI. Second, we shift from the single edge device setting to the distributed setting for the task of distributed on-device inference. I will focus on one killer application of edge computing, and present a distributed workload-adaptive framework for low-latency high-throughput large-scale live video analytics. Third, I will present our work on designing a distributed on-device training framework that significantly enhances the on-device training efficiency without compromising the training quality. Lastly, I will talk about our work on developing automated machine learning (AutoML) techniques to address the device deluge challenge which acts as one key barrier of achieving the vision of AIoT.
Biography: Mi Zhang is an Associate Professor and the Director of the Machine Learning Systems Lab at Michigan State University. He received his Ph.D. from University of Southern California and B.S. from Peking University. Before joining MSU, he was a postdoctoral scholar at Cornell University. His research lies at the intersection of mobile/edge/IoT systems and machine intelligence, spanning areas including On-Device/Edge AI, Automated Machine Learning (AutoML), Federated Learning, Systems for Machine Learning, Machine Learning for Systems, and AI for Health and Social Good. He has received a number of awards for his research. He is the 4th Place Winner of the 2019 Google MicroNet Challenge, the Third Place Winner of the 2017 NSF Hearables Challenge, and the champion of the 2016 NIH Pill Image Recognition Challenge. He is the recipient of seven best paper awards and nominations. He is also the recipient of the National Science Foundation CRII Award, Facebook Faculty Research Award, Amazon Machine Learning Research Award, and MSU Innovation of the Year Award.
Host: Pierluigi Nuzzo and Bhaskar Krishnamachari
Webcast: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQ
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar
Wed, Dec 01, 2021 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Emilie Dressaire, UCSB
Talk Title: Pushing boundaries: flow in low permeability media
Abstract: Generating and controlling fluid flow in low permeability environments is a challenge in natural and engineered systems. In this talk, I will discuss two studies involving the opening of fractures in a soft substrate and the clogging of microchannels.
The injection of fluid in brittle elastic materials drive the formation of cracks. Besides, when the pressure is released, the fluid flows out of the crack, in a process called backflow. Using a model experiment, we characterize the growth of a disk-like crack that propagates upon injection of the fluid, and its collapse as the injection pressure is released. The viscous dissipation, elastic deformation, and toughness of the matrix are important physical parameters that control the fluid flow in the crack or blister. This strategy is commonly used in rocks of low permeability and could find applications in bioengineering.
Yet the increase in permeability is only transient. A solution to avoid the closing of the crack formed by injection is to use suspensions of particles. However, the behavior of particles in confined systems remains mainly qualitative. I will discuss recent results obtained on the clogging of microchannels. When a suspension of particles flows in a microchannel, deposition and assembly can lead to the formation of a clog, followed by a stable aggregate of fixed porosity. I will present a model for the growth of the aggregate at the pore scale, which allows us to rationalize the evolution of the flow rate in networks of microchannels. Bridging the injection of fluid in elastic media with suspension dynamics is a promising route to advance printing in soft materials.
Biography: Emilie Dressaire received a B.S. in Engineering from ESPCI, France, in 2005, and a Ph.D. in Mechanical Engineering from Harvard University in 2009. She joined the Mechanical and Aerospace Engineering Department at NYU Tandon School of Engineering in 2014 and CNRS in 2017. She is now a faculty member in the Department of Mechanical Engineering at UCSB. She currently serves as a Member-at-Large on the Executive Committee of APS Division of Fluid Dynamics. Her research interests are centered around the areas of small scale fluid mechanics and soft matter physics, specifically focusing on interdisciplinary projects to develop bio-inspired methods to control and monitor fluid flows.
Host: AME Department
More Info: https://usc.zoom.us/j/97427241653?pwd=UGd2aXY2b3dsQkxMdzdvcnNBMjRJZz09
Webcast: https://usc.zoom.us/j/97427241653?pwd=UGd2aXY2b3dsQkxMdzdvcnNBMjRJZz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/97427241653?pwd=UGd2aXY2b3dsQkxMdzdvcnNBMjRJZz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/j/97427241653?pwd=UGd2aXY2b3dsQkxMdzdvcnNBMjRJZz09
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[Theory Seminar] Vaggos Chatziafratis (Northwestern Unviersity) - Hierarchical Clustering: Recent Progress and Open Questions
Thu, Dec 02, 2021 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Vaggos Chatziafratis, Northwestern University
Talk Title: Hierarchical Clustering: Recent Progress and Open Questions
Abstract: Hierarchical Clustering is an important tool for unsupervised learning whose goal is to construct a hierarchical decomposition of a given dataset describing relationships at all levels of granularity simultaneously. Despite its long history, Hierarchical Clustering was underdeveloped from a theoretical perspective, partly because of a lack of suitable objectives and algorithms with guarantees. In this talk, I want to tell you about the recent progress in the area with an emphasis on approximation algorithms and hardness results, and also highlight some interesting open problems.
Biography: Vaggos Chatziafratis' primary interests are in Algorithms and Machine Learning Theory. He is currently a postdoc at Northwestern and he will be a FODSI fellow at MIT and Northeastern starting January. He will also be joining UC Santa Cruz in Fall 2022 as an Assistant Professor.
Before that, he was at Google Research in New York, where he was part of the Algorithms and Graph Mining teams. Prior to that, he received his Ph.D. in Computer Science at Stanford, advised by Tim Roughgarden and co-advised by Moses Charikar. He received a Diploma in EECS from the National Technical University of Athens, Greece.
Host: Curtis Bechtel
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Astani Civil and Environmental Engineering Seminar
Thu, Dec 02, 2021 @ 11:00 AM - 12:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Kofi Christie, Postdoctoral Research Associate, Princeton University
Talk Title: Sustainable membrane-based carbon mineralization
Abstract: Please see attached abstract and bio.
Host: Dr. Amy Childress
Webcast: https://usc.zoom/j/99680049945? Meeting ID: 996 8004 9945 Passcode: 905716More Information: K. Christie-Abstract_Bio 12-02-2021.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 101
WebCast Link: https://usc.zoom/j/99680049945? Meeting ID: 996 8004 9945 Passcode: 905716
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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NL Seminar-Event Extraction and Reasoning in Multimedia News Data
Thu, Dec 02, 2021 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Manling Li, Univ of Illinois at Urbana-Champaign
Talk Title: Event Extraction and Reasoning in Multimedia News Data
Abstract: Reminder: Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you're highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we'll be aware of your attendance and let you in.Event understanding is an essential ability for humans to acquire information. With the rise of multimedia, automated event understanding and narration require machines to not only obtain the local structures of events from multimedia data i.e., who, what, where, and when), but also performs global understanding and inference i.e., what is likely to happen, and why. However, current event understanding is text-only, local, and lacks reasoning. Real events that are multimedia, interconnected, and probabilistic. This talk will present Multimedia Event Extraction to extract events and their arguments from multimedia data, and use event knowledge to enhance multimedia pretraining models. Based on the extracted knowledge, I will introduce how to induce event schemas (knowledge of complex event patterns) by learning a temporal graph model. After that, I will talk about how to use event knowledge to support real applications, such as timeline summarization.
Biography: Manling Li is a fourth year Ph.D. student at the Computer Science Department of University of Illinois Urbana Champaign. Manling has won the Best Demo Paper Award at ACL 20, the Best Demo Paper Award at NAACL 21, C.L. Dave and Jane W.S. Liu Award, and has been selected as Mavis Future Faculty Fellow. She is a recipient of the Microsoft Research PhD Fellowship. She has more than 30 publications on knowledge extraction and reasoning from multimedia data.
Host: Jon May and Thamme Gowda
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://youtu.be/MLITKOKIHY0Location: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://youtu.be/MLITKOKIHY0
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Dec 03, 2021 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jie Gu, Associate Professor, Northwestern University
Talk Title: Exploring New Dimensions of CMOS Deep Learning Accelerators with Neural CPU Architecture and Compute-in-Memory Circuits
Host: Mike Chen, Hossein Hashemi, Manuel Monge, Constantine Sideris
More Information: MHI IS Seminar - Jie Gu_Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
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LEAPFROG Lander Flight Ops | Call for Applications
Mon, Dec 06, 2021 @ 12:00 PM - 01:00 PM
Astronautical Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. David Barnhart, Director of SERC
Talk Title: LEAPFROG Lander Flight Ops | Call for Applications
Abstract: LEAPFROG is a unique prototype lander designed at USC and is an approved STEP Pilot program through NASA's Artemis in conjunction with UC San Diego and UC Berkeley. In Summer 2022, we will execute flight competitions where universities from around the country will have the opportunity to test their software on LEAPFROG landers. Up to three students will be eligible for paid internships for the SUMMER 2022 Flight Competition from USC. For more information, visit our website at either leapfrog.isi.edu or www.isi.edu/centers/serc/join_us
Virtual Event Link: https://usc.zoom.us/j/92579469862
Host: Prof. David Barnhart
Audiences: Everyone Is Invited
Contact: Dell Cuason/ Prof. Barnhart
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"DODONA": Intro to USC's 3rd Satellite
Mon, Dec 06, 2021 @ 02:00 PM - 03:00 PM
Astronautical Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. David Barnhart, Director of SERC
Talk Title: "DODONA": Intro to USC's 3rd Satellite
Abstract: A seminar presented by Space Engineering Research Center (SERC) team on USC's 3rd Satellite planned launch! "DODONA" is USC and the SERC's 3rd Satellite flight project in its history, slated to be launched early January 2022. This will be USC's first optical satellite launch and operations, and the SERC's first operation of the USC Ground Station to command the satellite from campus!
Virtual Event Link: https://usc.zoom.us/j/99363615054
Host: Prof. David Barnhart
Audiences: Everyone Is Invited
Contact: Dell Cuason/ Prof. Barnhart
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USC AI Futures Symposium on AI with Common SenseTue, Dec 07, 2021 @ 09:00 AM - 12:00 PM
Thomas Lord Department of Computer Science, Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Multiple panelists, USC and collaborators
Talk Title: AI with Common Sense
Series: USC AI Futures Symposium on AI
Abstract: Humans use common sense knowledge to understand how the world works. AI systems still lack common sense knowledge, which is critical in many domains in order to form expectations, manage unexpected situations, and connect with the human experience. This symposium presents an overview of research at USC on acquiring and organizing commonsense knowledge, integrating it into AI systems, and measuring its impact on improving AI system behaviors and interactions with people.
This event is part of the USC AI Futures Symposium Series. Prior events were held in May 2021 with the theme: AI and Data Science, and in January 2021 with the theme: Will AIs Ever Be One of Us?.
Please visit website to register and view updated information https://isi-usc-edu.github.io/USC-CommonSense-Symposium/
Host: Yolanda Gil
Location: Virtual
Audiences: Everyone Is Invited
Contact: Lori Weiss/USC ISI
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USC AI Futures Symposium on AI with Common Sense
Wed, Dec 08, 2021 @ 09:00 AM - 12:00 PM
Thomas Lord Department of Computer Science, Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Multiple panelists, USC and collaborators
Talk Title: AI with Common Sense
Series: USC AI Futures Symposium on AI
Abstract: Humans use common sense knowledge to understand how the world works. AI systems still lack common sense knowledge, which is critical in many domains in order to form expectations, manage unexpected situations, and connect with the human experience. This symposium presents an overview of research at USC on acquiring and organizing commonsense knowledge, integrating it into AI systems, and measuring its impact on improving AI system behaviors and interactions with people.
This event is part of the USC AI Futures Symposium Series. Prior events were held in May 2021 with the theme: AI and Data Science, and in January 2021 with the theme: Will AIs Ever Be One of Us?.
Please visit website to register and view updated information https://isi-usc-edu.github.io/USC-CommonSense-Symposium/
Host: Yolanda Gil
Location: Virtual
Audiences: Everyone Is Invited
Contact: Lori Weiss/USC ISI
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CS Colloquium: Yajie Zhao (USC ICT) - Automating 3D Contents Creation for AR/VR
Wed, Dec 08, 2021 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yajie Zhao, USC
Talk Title: Automating 3D Contents Creation for AR/VR
Abstract: The last couple of years have shown an incredible boost in performance, quality of real-time applications of AR/VR. The creation of high quality 3D assets, including virtual human, objects and 3D environments is tied with complex capture equipment, massive data, a long production cycle and intensive manual labor by a professional team. And it may still be in the notorious Uncanny Valley. In this talk, I will explore how to produce high quality 3D assets in a low-cost way. I will discuss how to leverage AI-based technologies to automate, accelerate, and simplify the industrial production procedure of 3D contents creation from data capturing to rendering. And bring photorealism to the next level!
This lecture satisfies requirements for CSCI 591: Research Colloquium
Join Zoom Meeting
https://usc.zoom.us/j/97098103729
Meeting ID: 970 9810 3729
Biography: Dr. Yajie Zhao is a computer scientist at USC Institute for Creative Technologies (ICT), University of Southern California. She is currently the acting director of the Vision and Graphics Lab of USC-ICT. Her research interests are high-quality 3D content creation for AR/VR, which includes human digitization, performance capturing, and scene reconstruction/ understanding. Yajie Zhao earned her Ph.D. degree in 2017 from the University of Kentucky under the supervision of Dr. Ruigang Yang.
Host: Aram Galstyan
Audiences: Everyone Is Invited
Contact: Cherie Carter
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Medical Imaging Seminar Series
Fri, Dec 10, 2021 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Talk Title: Improved Regularized Simultaneous Multi-slice (SMS) Imaging Reconstruction
Series: Medical Imaging Seminar Series
Abstract: MRI acquisitions are inherently slow, necessitating the use of accelerated imaging. Simultaneous multi- slice (SMS) imaging has gained substantial interest by providing improved coverage with minimum signal- to-noise ratio (SNR) loss in accelerated MRI and has been widely integrated into large-scale projects such as Human Connectome Project. However, ultra-high accelerations are prone to noise amplification and residual aliasing artifacts, necessitating new reconstruction techniques that can successfully suppress both. In this talk, we will present recently developed techniques for regularized SMS reconstruction. We will first introduce two model-based algorithms that simultaneously reduce noise amplification and inter-leakage artifacts. Subsequently, we will concentrate on physics-guided deep learning reconstruction for SMS MRI with applications in fMRI. Finally, we will discuss an alternative way to view the multi-coil encoding operator in physics-guided DL reconstruction for improved generalizability in dynamic contrast-enhanced MRI.
Biography: Omer Burak Demirel is a PhD candidate at the University of Minnesota working with Prof. Mehmet Akçakaya. Prior to the University of Minnesota, he received the B.S. and M.S. degrees from Bilkent University, Ankara, Turkey in January 2015 and June 2017, respectively. His research interests include image processing, MRI acquisition methods, image reconstruction techniques and accelerated MRI. He is a recipient of an AHA predoctoral fellowship focusing on improved image reconstruction techniques for cardiac MRI.
Host: Krishna Nayak, knayak@usc.edu
Webcast: https://usc.zoom.us/j/95249648177?pwd=RHNsSnlhMk0vaEtPeExXRkRPOE55dz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/95249648177?pwd=RHNsSnlhMk0vaEtPeExXRkRPOE55dz09
Audiences: Everyone Is Invited
Contact: Talyia White
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Astani Department of Civil and Environmental Engineering Ph.D. Dissertation
Mon, Dec 13, 2021 @ 04:00 PM - 06:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Preetham Aghalaya Manjunatha , Astani Department of Civil and Environmental Engineering
Talk Title: Vision-Based and Data-driven Analytical and Experimental Studies into Condition Assessment and Change Detection of Evolving Civil, Mechanical and Aerospace Infrastructures
Abstract: Civil, mechanical, and aerospace infrastructures are subjected to applied loads and environmental forces like earthquakes, wind, and water waves in their operating lifespan. These factors will slowly deteriorate the structures during their service period, and often subtle observations of substantial damages are challenging. Due to the cost-effectiveness of high-resolution color, depth cameras, location sensors, and Micro Aerial Vehicles (MAVs), image processing, computer vision, and robotics techniques are gaining interest in Non-Destructive Testing (NDT) and condition assessment of infrastructures. In this study, several promising vision-based and data-driven, automated, and semi-automated condition assessment techniques are proposed and evaluated to detect and quantify a class of problems under the umbrella of infrastructure condition assessment.
A synthetic crack generation methodology is introduced to generate zero-labeled samples for training the classical classifiers. This classifier was tested on a real-world dataset using the gradient-based hierarchical hybrid Multi-scale Fractional Anisotropy Tensor (MFAT) filter to segment the cracks. The results demonstrate the promising capabilities of the proposed synthetic crack generation method. Furthermore, the textural noise suppression and refinement are carried out by using an anisotropic diffusion filter. Guidelines are provided to select the parameters for the anisotropic diffusion filter. Further, this study presents the semantic segmentation of the cracks on concrete surface images using a deep Convolutional Neural Network (CNN) that has fewer parameters to learn. Several illustrative examples are presented to demonstrate the capabilities of the CNN-based crack segmentation procedure. The CNN was tested on the four real-world datasets, and the results show the proposed CNN's superiority against four state-of-the-art methods.
As a part of this study, an efficient and autonomous crack change detection, tracking, and evolution methodology is introduced. Among the image registration methods, feature-based registration is robust to the noise, intensity change, and partial affine motion model. This study uses an efficient $k$-d tree-based nearest neighbor search which is faster than the quadratic computational complexity of the current pairwise search. Furthermore, unlike other methods, the fixed camera assumption is relaxed in this study. Another significant contribution is a probabilistic measure of the reliability of the analysis results that can aid the prognosis damage detection models.
After the nearest neighbor search, the SURF-based keypoints are extracted from the images in the previous database and the current one. This is followed by the Random Sample Consensus (RANSAC)-based outliers rejection, bundle adjustment to refine the homographies, gain exposure compensation and multi-band blending for the seamless registration images. Lastly, the registered image is compared to the current images for the change detection in crack physical properties. To demonstrate the capabilities of the proposed method, two datasets were utilized; a real-world dataset, and a synthetic dataset. The experimental results show that the performance of the proposed methodology is suitable for detecting the crack changes in two datasets.
This work also studies the condition assessment of public sewer pipelines. The visual-bags-of-words model was evaluated for classifying the defective and non-defective sewer pipeline images using two feature descriptors. Three classical classifiers are trained and tested on a moderate-sized dataset of 14,404 images. The experimental results demonstrate that the classification accuracy of the visual-bags-of-words model is satisfactory and comparable to deep learning methods given the moderate dataset size.
Lastly, defect detection of the three-dimensional surface of mechanical parts is studied. A preliminary study on a vision-based semi-autonomous spatio-temporal method to detect, locate and quantify the defects such as loose bolts, displacements, pipe chafing, or deformation is proposed. In addition, a probabilistic reliability quantification method based on the ensemble averaging of the Cloud-to-Cloud (C2C) distances is introduced for mechanical systems. Several quantitative and qualitative examples are presented to illustrate the capabilities of the proposed method. The results show that the proposed method is promising and robust to register the complex shapes, and detect and locate the changes in the mechanical systems.
Webcast: https://usc.zoom.us/j/98823087239? Meeting ID: 988 2308 7239 Passcode: 729479WebCast Link: https://usc.zoom.us/j/98823087239? Meeting ID: 988 2308 7239 Passcode: 729479
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Astani Department of Civil and Environmental Engineering Ph.D. Dissertation
Wed, Dec 15, 2021 @ 12:00 PM - 02:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaoshu Zeng, CE Ph.D. Candidate, Viterbi School- Astani Department of Civil and Environmental Engineering
Talk Title: Efficient Inverse Analysis with Dynamic and Stochastic Reductions for Large-Scale Models of Multi-Component Systems
Abstract: This work has two main goals: the primary goal of dealing with inverse analysis for large-scale models of multi-component systems and the second goal of dealing with the multi-fidelity uncertainty
quantification (UQ) for models with dissimilar parameterization. The primary goal involves efficient structural dynamic analysis, probabilistic modeling, and inverse analysis for the following reasons:
1. The context is the integrity assessment of the internals of a complex multi-scale structure, a fullyloaded spent nuclear fuel canister (FLSNFC), with the assessment based on dynamic signals on the
exterior surface of the FLSNFC before and after transportation.
2. Since the observed data is inevitably subjected to errors, the inverse analysis usually requires an accurate probabilistic model to capture the uncertainty propagated to the quantities of interest (QoI).
3. An efficient forward model, a dynamic model, is essential to construct a probabilistic model for complex systems.
A first attempt to build an efficient dynamic model is through constructing a global reduced-order basis (ROB). Usually, the complex multi-scale structures are characterized by numerous local vibration modes (or elastic modes) and the usual long-wavelength global vibration modes. Accordingly, a methodology that does not require the computation of the numerous local modes builds a global reduced-order model (ROM) by constructing a global ROB. In this method, the kinematics of the structure is modified to filter the local vibrations. Moreover, the reduced kinematics is combined with the idea of static condensation to achieve higher accuracy.
Due to the high accuracy requirement, a second attempt for dynamic modeling is carried out. For the FLSNFC, a honeycomb basket is placed inside the cylindrical canister, and a fuel assembly (FA)
that holds nearly 100 fuel rods is inserted in each of the 68 basket cells. A multi-level nested CraigBampton (CB) sub-structuring method with shift-invert Lanczos (SIL) eigenvalue solver and filtering of the local vibration modes of the substructures is proposed. This method is adapted to the multi-scale nature and localized connections between the substructures. The CB sub-structuring technique takes advantage of the limited degrees of freedom (DOF) of internal boundary and is applied to modal analysis for two structural levels, the system and the FA levels. As a result, the integrated method achieves a computational gain of four orders of magnitude for the FLSNFC at the expense of negligible errors.
For probabilistic modeling, polynomial chaos expansion (PCE) is an efficient method, but it suffers from the curse of dimensionality. However, a basis adaptation method proposed by Tipireddy and Ghanem (2014) [6] can reduce the dimension of the problem by rotating the input Gaussian random variables such that the quantity of interest (QoI) in the new space has concentrated representation. In this study, we proposed two novel approaches that can accelerate the convergence of the basis adaptation method. In the first approach, the mean and Gaussian coefficients in the adapted space are corrected by information obtained from a pilot PCE. The second approach updates the rotation matrix by taking advantage of the probabilistic information embedded in the higher dimensional adaptation gleaned from an initial adaptation. As a result, both approaches achieve accelerated convergence of the basis adaptation method with negligible additional costs.
The basis adaptation is adequate to reduce the dimension for the scalar QoI problems. To deal with UQ problems with high dimensional QoI and high dimensional parameter space, the integration of Karhunen- Loève expansion (KLE) and basis adaptation is proposed. The KLE first approximates the QoI to reduce the dimension of the QoI to a limited number of KL terms. Then, for each KL term, an adapted PCE is built with the accelerated basis adaptation method to reduce the dimension of the input variables. The PCE models of the KL terms then can be substituted back to the KLE of the QoI to obtain a surrogate probabilistic model of the QoI. Finally, the accuracy of the surrogate model is verified.
The Bayesian method will be used for the inverse analysis of parameter inference given observed data of a possibly damaged model. The process usually involves evaluating the forward model
numerous times, which is intractable for the complex system considered in the present study, even if the dynamic ROM is used. Thanks to the accurate surrogate probabilistic model of the QoI, the physical model required to be evaluated in Bayesian analysis can be replaced by the surrogate model to achieve several orders of computational efficiency. Nevertheless, generating posterior samples of high dimensional parameters can still be challenging. Thus, a block-update Markov Chain Monte Carlo (MCMC) method is applied to address this issue. By appropriately designing the inverse problem, the location and damage types of the internals can be identified based on dynamic signals on the exterior
surface.
The second goal of the work is to propose a novel multi-fidelity UQ method for dissimilar parameterization models. In multi-fidelity UQ, credible prediction and analyses of high-fidelity (HF) models are obtained by leveraging evaluations of a large number of efficient low-fidelity (LF) models.
The efficacy of the technique relies heavily on the correlation of the HF and LF models. We propose using the basis adaption method in the multi-fidelity technique to independently identify the important directions (or adapted variables) for each model, and the important directions assemble a common lower-dimensional space. Since important directions have concentrated information of the QoI and are aligned for different models, the samples generated on the common lower-dimensional manifold have
enhanced correlations. Thus, the proposed method can increase the performance of the multi-fidelity technique, especially for models with dissimilar parameterization.
The two main goals in the thesis are relevant since both involve stochastic reduction for complex multi- component models.
Webcast: Join Zoom Meeting https://usc.zoom.us/j/95953335264?pwd=SnNEY3JST0hVVFZCdzg2MWpwOG5zUT09 Meeting ID: 959 5333 5264 Passcode: 275995Location: Zoom Meeting
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
