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Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Alex Beatson, Priya Donti, Amira Abdel-Rahman, Stephan Hoyer, Rose Yu, J. Zico Kolter, Ryan Adams

2020-12-12T04:50:00-08:00 - 2020-12-12T15:00:00-08:00
Abstract: For full details see:

Modern engineering workflows are built on computational tools for specifying models and designs, for numerical analysis of system behavior, and for optimization, model-fitting and rational design. How can machine learning be used to empower the engineer and accelerate this workflow? We wish to bring together machine learning researchers and engineering academics to address the problem of developing ML tools which benefit engineering modeling, simulation and design, through reduction of required computational or human effort, through permitting new rich design spaces, through enabling production of superior designs, or through enabling new modes of interaction and new workflows.


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2020-12-12T04:50:00-08:00 - 2020-12-12T05:00:00-08:00
Opening Remarks
2020-12-12T05:00:00-08:00 - 2020-12-12T05:30:00-08:00
Nils Thuerey - Lead the Way! Deep Learning via Differentiable Simulations
Nils Thuerey
2020-12-12T05:30:00-08:00 - 2020-12-12T05:40:00-08:00
Nils Thuerey Q&A
2020-12-12T05:40:00-08:00 - 2020-12-12T06:10:00-08:00
Angela Dai - Self-supervised generation of 3D shapes and scenes
Angela Dai
2020-12-12T06:10:00-08:00 - 2020-12-12T06:20:00-08:00
Angela Dai Q&A
2020-12-12T06:20:00-08:00 - 2020-12-12T08:20:00-08:00
Poster Session 1
2020-12-12T08:20:00-08:00 - 2020-12-12T08:50:00-08:00
Tatiana Lopez-Guevara - Robots, Liquids & Inference
Tatiana Lopez-Guevara
2020-12-12T08:50:00-08:00 - 2020-12-12T09:00:00-08:00
Tatiana Lopez-Guevara Q&A
2020-12-12T09:00:00-08:00 - 2020-12-12T09:30:00-08:00
Peter Battaglia - Structured models of physics, objects, and scenes
Peter Battaglia
2020-12-12T09:30:00-08:00 - 2020-12-12T09:40:00-08:00
Peter Battaglia Q&A
2020-12-12T09:40:00-08:00 - 2020-12-12T10:30:00-08:00
2020-12-12T10:30:00-08:00 - 2020-12-12T11:30:00-08:00
Panel discussion with invited speakers
2020-12-12T11:30:00-08:00 - 2020-12-12T12:00:00-08:00
Karen E Willcox - Operator Inference: Bridging model reduction and scientific machine learning
Karen Willcox
2020-12-12T12:00:00-08:00 - 2020-12-12T12:10:00-08:00
Karen E Willcox Q&A
2020-12-12T12:10:00-08:00 - 2020-12-12T12:40:00-08:00
Grace X Gu - Artificial intelligence for materials design and additive manufacturing
Grace Gu
2020-12-12T12:35:00-08:00 - 2020-12-12T12:50:00-08:00
Grace X Gu Q&A
2020-12-12T12:50:00-08:00 - 2020-12-12T13:00:00-08:00
Closing remarks
2020-12-12T13:00:00-08:00 - 2020-12-12T15:00:00-08:00
Poster Session 2
Signal Enhancement for Magnetic Navigation Challenge Problem
Albert Gnadt, Joseph Belarge, Aaron Canciani, Lauren Conger, Joseph Curro, Alan Edelman, Peter Morales, Mike O'Keeffe, Jonathan Taylor, Christopher Rackauckas
Parameterized Reinforcement Learning for Optical System Optimization
Heribert Wankerl, Maike Stern, Ali Mahdavi, Christoph Eichler, Elmar Lang
Efficient nonlinear manifold reduced order model
Youngkyu Kim, Youngsoo Choi, David Widemann, Tarek Zohdi
Jacobian of Conditional Generative Models for Sensitivity Analysis of Photovoltaic Device Processes
Maryam Molamohammadi, Sahand Rezaei-Shoshtari, Nathaniel Quitoriano
Differentiable Implicit Layers
Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters
Decoding the genome of cement by Gaussian Process Regression
Yu Song, Yongzhe Wang, Kaixin Wang, Mathieu Bauchy
Exact Preimages of Neural Network Aircraft Collision Avoidance Systems
Kyle Matoba, François Fleuret
Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models
Benjamin Choi, Alex Nutkiewicz, Rishee Jain
Prediction of high frequency resistance in polymer electrolyte membrane fuel cells using Long Short Term Memory based model
Tong Lin
Flaw Detection in Metal Additive Manufacturing Using Deep Learned Acoustic Features
Wentai Zhang, Levent Burak Kara
Constraint active search for experimental design
Gustavo Malkomes, Harvey Cheng, Michael McCourt
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Zijiang Yang, Dipendra Jha, Arindam Paul, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Robotic gripper design with Evolutionary Strategies and Graph Element Networks
Ferran Alet, Maria Bauza, Adarsh K Jeewajee, Max Thomsen, Alberto Rodriguez, Leslie Kaelbling, Tomás Lozano-Pérez
Model Order Reduction using a Deep Orthogonal Decomposition
Daniel Tait
Analog Circuit Design with Dyna-Style Reinforcement Learning
Wook Lee, Frans Oliehoek
Continuous calibration of a digital twin; a particle filter approach
Rebecca Ward, Ruchi Choudhary, Alastair Gregory
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing
Dhruv Vashisht, Harshit, Preferred: Harshik Rampal, Haiguang Liao, Yang Lu, Devika B Shanbhag, Elias Fallon, Levent Burak Kara
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
TPINN: An improved architecture for distributed physics informed neural networks
Sreehari Manikkan, Balaji Srinivasan
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Ruisheng Diao, Di Shi, Bei Zhang, Siqi Wang, Haifeng Li, Chunlei Xu, Tu Lan, Desong Bian, Jiajun Duan, Zheng Wu
ManufacturingNet: A machine learning tool for engineers
Rishikesh Magar, Lalit Ghule , Ruchit Doshi, Sharan Seshadri , Aman Khalid, Amir Barati Farimani
Building LEGO using Deep Generative Models of Graphs
Rylee Thompson, Graham W Taylor, Terrance DeVries, Elahe Ghalebi
Learning Mesh-Based Simulation with Graph Networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez Gonzalez, Peter Battaglia
Scalable Multitask Latent Force Models with Applications to Predicting Lithium-ion Concentration
Daniel Tait, Ferran Brosa-Planella, Widanalage Dhammika Widanage, Theo Damoulas
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Xiren Zhou, siqi wang, Ruisheng Diao, Desong Bian, Jiajun Duan, Di Shi
Learning to Identify Drilling Defects in TurbineBlades with Single Stage Detectors
Andrea Panizza, Szymon Tomasz Stefanek, Stefano Melacci, giacomo Veneri, Marco Gori
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Tongtao Zhang, Biswadip Dey, Pratik Kakkar, Amit Chakraborty
Scalable Combinatorial Bayesian Optimization with Tractable Statistical models
Aryan Deshwal, Syrine Belakaria, Jana Doppa
Combinatorial 3D Shape Generation via Sequential Assembly
Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee, Minsu Cho, Jaesik Park
Accelerating Inverse Design of Nanostructures Using Manifold Learning
Mohammadreza Zandehshahvar, Yashar Kiarashinejad, Muliang Zhu, Hossein Maleki, Omid Hemmatyar, Sajjad Abdollahramezani, Reza Pourabolghasem, Ali Adibi
A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow
Kyri Baker
Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort
Alok Warey, Shailendra Kaushik, Bahram Khalighi, Michael Cruse, Ganesh Venkatesan
Surrogates for Stiff Nonlinear Systems using Continuous Time Echo State Networks
Ranjan Anantharaman, Chris V Rackauckas, Viral Shah
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Sirui Bi, Jiaxin Zhang, Guannan Zhang
On the Effectiveness of Bayesian AutoML methods for Physics Emulators
Peetak Mitra, Niccolo Dal Santo, Majid Haghshenas, Shounak Mitra, Conor Daly, David Schmidt
Machine Learning-based Anomaly Detection with Magnetic Data
Peetak Mitra, Denis Akhiyarov, Mauricio Araya-Polo, Daniel Byrd
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Mikkel Lykkegaard, Greg Mingas, Robert Scheichl, Colin Fox, Tim Dodwell
An adversarially robust approach to security-constrained optimal power flow
Neeraj Vijay Bedmutha, Priya Donti, J. Zico Kolter
A data centric approach to generative modelling of rough surfaces: An application to 3D-printed Stainless Steel
Liam Fleming
Efficient Nanopore Optimization by CNN-accelerated Deep Reinforcement Learning
Yuyang Wang, Zhonglin Cao, Amir Barati Farimani
A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics
Sirui Bi, Jiaxin Zhang, Guannan Zhang
Real-time Prediction of Soft Tissue Deformations Using Data-driven Nonlinear Presurgical Simulations
Haolin Liu, Ye Han, Daniel Emerson, Houriyeh Majditehran, Yoed Rabin, Levent Burak Kara
Multi-stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents
Xiumin Shang, Jingping Yang, Bingquan Zhu, Lin Ye, Jing Zhang, Jianping Xu, Qin Lyu, Ruisheng Diao
Probabilistic Adjoint Sensitivity Analysis for Fast Calibration of Partial Differential Equation Models
Jon Cockayne, Andrew Duncan
Data-driven inverse design optimization of magnetically programmed soft structures
Alp Karacakol, Yunus Alapan, Metin Sitti
Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning
Xiaoying Pang, Sunil Thulasidasan, Larry Rybarcyk
Multi-Loss Sub-Ensembles for Accurate Classification with Uncertainty Estimation
Omer Achrack, Raizy Kellerman, Ouriel Barzilay
End-to-End Differentiability and Tensor Processing Unit Computing to Accelerate Materials’ Inverse Design
HAN LIU, Yuhan Liu, Zhangji Zhao, Sam Schoenholz, Dogus Cubuk, Mathieu Bauchy
Bayesian polynomial chaos
Pranay Seshadri, Andrew Duncan, Ashley Scillitoe
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
Syrine Belakaria, Aryan Deshwal, Jana Doppa
Collaborative Multidisciplinary Design Optimization with Neural Networks
Jean de Becdelievre, Ilan Kroo
Modular mobile robot design selection with deep reinforcement learning
Julian Whitman, Matthew Travers, Howie Choset
A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings
Yongchao Huang, Hugh Miles, Pengfei Zhang
Heat risk assessment using surrogate model for meso-scale surface temperature
Byeongseong Choi, Matteo Pozzi, Mario Berges
Predicting Nanorobot Shapes via Generative Models
Emma Benjaminson, Rebecca Taylor, Matthew Travers
An Industrial Application of Deep Reinforcement Learning for Chemical Production Scheduling
Christian Hubbs, Adam Kelloway, John Wassick, Nikolaos Sahinidis, Ignacio Grossmann
Simultaneous Process Design and Control Optimization using Reinforcement Learning
Steven Sachio, Antonio del Rio Chanona, Panagiotis Petsagkourakis
Battery Model Calibration with Deep Reinforcement Learning
Ajaykumar Unagar, Yuan Tian, Olga Fink, Manuel Arias Chao
Uncertainty-aware Remaining Useful Life predictors
Luca Biggio, Manuel Arias Chao, Olga Fink