Name: SAMM 2020: Learning Models from Data
Start: 2020-07-27T13:30:00+02:00
End: 2020-07-31T19:45:00+02:00
Location: Virtual event

SAMM 2020: Learning Models from Data

from Monday 27 July 2020 (13:30) to Friday 31 July 2020 (19:45)

Monday 27 July 2020 ¶

13:40 Opening
Opening
13:40 - 14:00
14:00 Lecture/exercises 1 - Feliks Nüske (Paderborn University)
Lecture/exercises 1
- Feliks Nüske (Paderborn University)
14:00 - 15:30
Introduction to Stochastic Dynamics and Transition Operators - Markov Property and Transition Kernels - Perron-Frobenius and Koopman Operator - Examples (ODEs, Brownian Motion) - Stationarity, Reversibility
16:00 Lecture/exercises 1 - Benjamin Peherstorfer (New York University)
Lecture/exercises 1
- Benjamin Peherstorfer (New York University)
16:00 - 17:30
Introduction to traditional (intrusive, projection-based) model reduction - POD/PCA, greedy - Galerkin ROMs - DEIM
18:00 Lecture/exercises 1 - J. Nathan Kutz (University of Washington)
Lecture/exercises 1
- J. Nathan Kutz (University of Washington)
18:00 - 19:30
Introduction to data-driven modeling - DMD - Koopman - Time-delay embeddings (HAVOK)
19:30
19:30 - 20:30
Contributions

19:30 Optimizing Intense Laser-Plasma Interactions with Evolutionary Algorithms and Machine Learning - Joseph Smith (The Ohio State University)

19:30 Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks - Colby Wight (PNNL)

19:30 Learning Constitutive Relations using Symmetric Positive Definite Neural Networks - Kailai Xu (Stanford University)

19:30 Nonlinear model reduction for one-dimensional solidification process in additive manufacturing - Parisa Khodabakhshi (Oden Institute for Computational Engineering and Sciences, UT Austin)

19:30 Analysis of bubble dynamics using data-driven dynamical systems and machine learning - Andrew J. Gibson (Department of Mechanical and Aerospace Engineering, University of Colorado Colorado Springs)

19:30 Analyzing the Transition to Buffeting of a 2D Airfoil using the Dynamic Mode Decomposition - Sathsara Dias (Clarkson University)

19:30 Discovering the governing PDE of an active nematic system from video data - Connor Robertson (New Jersey Institute of Technology)

19:30 Extrapolating Nuclear Masses using Bayesian Gaussian Process Regression - Rahul Jain (Michigan State University )

19:30 System Identification by Sparse Bayesian Learning - Luning Sun (University of Notre Dame)

19:30 Machine learning for parameters identification in structural joints models - Simone Gallas (KU Leuven, Department of Mechanical Engineering, Division LMSD; Core Lab DMMS-D, Flanders Make)

19:30 Analytical and Learning Model of a Hybrid-Fluidic Elastomer Actuator for Reliable Control and Perturbation Detection - Uksang Yoo (The University of Texas at Austin)

19:30 An optimization-based approach for the reduction of parametrized conservation laws with discontinuities - Marzieh Alireza Mirhoseini (University of Notre Dame)

19:30 Analytical Modeling and Evaluation of Curvature-Dependent Distributed Friction Force in Tendon-Driven Continuum Manipulators - Yang Liu (The University of Texas at Austin)

19:30 Learning the Interfacial Area Equation from Data - Stephen Chen (University of California San Diego)

19:30 Analytical and Data-driven Models to Predict Algae Biofilm Growth in Water Treatment - Gerald Jones (Utah State University)

19:30 PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain - Han Gao (University Of Notre Dame)

19:30 MUQ-hIPPYlib: A Bayesian Inference Software Framework Integrating Data with Complex Predictive Models under Uncertainty - Ki-Tae Kim (University of California, Merced)

19:30 Learning age-related chronic disease progression from cognitive measurements - Cindy Catherine Orozco Bohorquez (Stanford University)

19:30 Modulus-based iterative methods for constrained $ℓ_{p}$ - $ℓ_{q}$ minimization - Mirjeta Pasha (Arizona State University)

19:30 A function space random feature model for PDE solution maps - Nicholas H. Nelsen (Caltech)

19:30 Weak SINDy: Galerkin-Based Data-Driven Model Selection - Daniel Messenger (University of Colorado Boulder)
Tuesday 28 July 2020 ¶
14:00 Lecture/exercises 2 - Feliks Nüske (Paderborn University)
Lecture/exercises 2
- Feliks Nüske (Paderborn University)
14:00 - 15:30
Stochastic Calculus - Stochastic Integral and SDEs - Ito’s Formula - The Generator of an SDE - Examples
16:00 Lecture/exercises 2 - Benjamin Peherstorfer (New York University)
Lecture/exercises 2
- Benjamin Peherstorfer (New York University)
16:00 - 17:30
- Intrusive ROMs: Error bounds: residual-based; offline-online splitting - Recovering reduced models from data with operator inference (re-projection) and relation to DMD
18:00 Lecture/exercises 2 - J. Nathan Kutz (University of Washington)
Lecture/exercises 2
- J. Nathan Kutz (University of Washington)
18:00 - 19:30
Introduction to DMD/Koopman for ROMs data-driven modeling - DMD integration into Galerkin ROMs - Koopman/DMD models for ROMs
19:30 Social event
Social event
19:30 - 20:30
Wednesday 29 July 2020 ¶
14:00 Lecture/exercises 3 - Feliks Nüske (Paderborn University)
Lecture/exercises 3
- Feliks Nüske (Paderborn University)
14:00 - 15:30
(E)DMD for Stochastic Dynamics - Basic EDMD - Variational Principle for Reversible Dynamics - Generator EDMD
16:00 Lecture/exercises 3 - Benjamin Peherstorfer (New York University)
Lecture/exercises 3
- Benjamin Peherstorfer (New York University)
16:00 - 17:30
- Operator inference (non-Markovian terms for partial observations) - Probabilistic generalization bounds in limited situations for operator inference
18:00 Lecture/exercises 3 - J. Nathan Kutz (University of Washington)
Lecture/exercises 3
- J. Nathan Kutz (University of Washington)
18:00 - 19:30
Introduction to neural networks for DMD & Koopman approximations - NN for learning coordinate transformations - Koopman reductions more linear ROMs
19:30
19:30 - 20:30
Contributions

19:30 One size does not fit all: Parameterized biomechanical models for crashworthiness simulations - Göktürk Kuru (Siemens Digital Industries Software)

19:30 Wavelet based dynamic mode decomposition - Manu Krishnan (PhD Candidate, Department of Aerospace and Ocean Engineering, Virginia Tech)

19:30 Adaptive Interpolatory MOR by Learning the Error Estimator in the Parameter Domain - Sridhar Chellappa (Max Planck Institute for Dynamics of Complex Technical Systems)

19:30 Sensor selection for hyper-parameterized linear Bayesian inverse problems - Nicole Aretz-Nellesen (RWTH Aachen)

19:30 Data-Driven Identification and Reduction of Dynamical Systems with the Loewner Framework - Dimitrios S. Karachalios (MPI-DRI)

19:30 Artificial neural network for bifurcating phenomena modelled by nonlinear parametrized PDEs - Federico Pichi (SISSA, International School for Advanced Studies)

19:30 Learning from Data for Traffic Control - Urs Baumgart (Fraunhofer ITWM)

19:30 Multipopulation mortality rates modelling and forecasting: The multivariate functional principal component analysis approach - Ka Kin Lam (University of Leicester)

19:30 Deep learning of multibody minimal coordinates for estimation - Andrea Angeli (KU Leuven, Flanders Make)

19:30 A Hamiltonian Monte Carlo Bayesian Inference Approach Using Deep Learning for Modeling Metabolism - Christina Schenk (Basque Center for Applied Mathematics)

19:30 Real-time virtual acoustics using physics-informed data-driven techniques - Finnur Pind (Technical University of Denmark)

19:30 Machine learning for parameter identification and model reduction of gradient-enhanced damage models for metal forming processes - Robin Schulte (Institute of Mechanics, TU Dortmund University)

19:30 Model adaptation for hyperbolic balance laws employing constraint aware neural networks - Hrishikesh Joshi (Technical University of Darmstadt)

19:30 Towards Deep Learning Based Controllers with Nominal Closed Loop Stability Guarantees - Hoang Hai Nguyen (Otto-von-Guericke University Magdeburg, Magdeburg, Germany)

19:30 Convolutional Neural Networks for object detection in professional appliances - Laura Meneghetti (SISSA)

19:30 Kernel-based Active Subspaces with application to CFD problems using Discontinuous Galerkin method - Francesco Romor (SISSA)

19:30 Deep Neural Networks for Hyperbolic Conservation laws with Non-convex Flux - Hadi Minbashian (Technical University of Darmstadt)

19:30 Data-driven reduced-order modeling from noisy measurements: new results and future perspectives - Ion Victor Gosea (MPI Magdeburg)

19:30 Prediction-Based Nature-Inspired Dynamic Optimization - Almuth Meier (SCHOTT AG)

19:30 Reduced order modelling for data assimilation in parametrized optimal control framework - Maria Strazzullo (SISSA, mathLab)

19:30 Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy - Maik Pfefferkorn (Otto-von-Guericke University Magdeburg)

19:30 Data-driven metamodelling in Global Sensitivity Analysis - Panagiotis Demis (Department of Chemical and Process Engineering, University of Surrey)
Thursday 30 July 2020 ¶
13:00
13:00 - 14:00
Contributions

13:00 Deep learning based model reduction approaches in flow models - Yiran Wang (The Chinese University of Hong Kong)

13:00 The use of machine learning in Computational Fluid Dynamics for an economic approach to flow optimization problems. - Georg Schatzdorfer (TU Graz)

13:00 Deep learning of superstructures in turbulence - Manuel Schaller (TU Ilmenau) Friedrich Philipp (TU Ilmenau) Mitsuru Wilson (TU Ilmenau)

13:00 The Construction and Application of Surrogate Models for Sensitivity Analysis - Xifu Sun (Australian National University)

13:00 Data-based soil-tool interaction force prediction based on measurements and the Discrete Element Method - Jonathan Jahnke (Fraunhofer ITWM)

13:00 Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics - Jonas Kneifl (Institute of Engineering and Computational Mechanics)

13:00 Basis Generation Techniques for Symplectic Model Order Reduction - Patrick Buchfink (University of Stuttgart)

13:00 Physics Guided Deep-Learning Based Nonlinear Reduced Order Model for Aeroelastic Applications - Rahul Halder (National University of Singapore)

13:00 Enhancing battery recharge performance by combined Machine Learning and PDE modelling - Angela Monti (University of Salento)

13:00 Stochastic frequency domain surrogate models for linear structural dynamics - Felix Schneider (Technical University of Munich)

13:00 Data-driven Reduced Order Model of Flow-Induced Piezoelectric Energy Harvesters - Lan Shang (University of Luxembourg)

13:00 Data-Driven Learning of Reduced-Order Dynamics for a Parametrized Shallow Water Equation - Süleyman Yildiz (Institute of Applied Mathematics, Middle East Technical University, Ankara, Turkey)

13:00 Deep Kernel approaches with a Neural-Network-like structure - Tizian Wenzel (University of Stuttgart)

13:00 Chance-constrained optimal control of hyperbolic supply systems - Kerstin Lux (Technical University of Munich)

13:00 Stochastic Grey-box Model of the Flow-Front Dynamics - Rishi Relan (Siemens Energy, Technical University of Denmark, BML Munjal University)

13:00 Data-driven computational continuum mechanics - Vu Chau (University of Luxembourg )

13:00 Quantifying incompressible two-phase flow fields from the interface movement using physics-informed neural networks - Aaron Buhendwa (Technical University Munich, Chair of Aerodynamics and Fluid mechanics)

13:00 A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks - Deniz A Bezgin (Technical University of Munich)

13:00 Model Reduction for Advection Dominated Problems - Davide Torlo (University of Zurich)

13:00 Data-based Approach for Fault Diagnosis of Hydropower Rotors - Christian Sperber (Voith Hydro Holding GmbH & Co. KG)

13:00 Insights into squealing disk brakes through explainable machine learning for time series data - Merten Stender (Hamburg University of Technology)

13:00 Can machine learning methods be used to create parametrized reduced models of vibro-acoustic systems? - Quirin Aumann (Technical University of Munich, Chair of Structural Mechanics)
14:00 Lecture/exercises 4 - Feliks Nüske (Paderborn University)
Lecture/exercises 4
- Feliks Nüske (Paderborn University)
14:00 - 15:30
Introduction to Kernel Methods for Data Driven Modeling - Kernel-based EDMD and Generator EDMD
16:00 Lecture/exercises 4 - Benjamin Peherstorfer (New York University)
Lecture/exercises 4
- Benjamin Peherstorfer (New York University)
16:00 - 17:30
- Interpolatory model reduction: transfer function, H2 norm, Loewner - Learning in the frequency domain: Loewner noise, AAA, vector fitting
17:30 Virtual group photo
Virtual group photo
17:30 - 17:35
18:00 Lecture/exercises 4 - J. Nathan Kutz (University of Washington)
Lecture/exercises 4
- J. Nathan Kutz (University of Washington)
18:00 - 19:30
Introduction to data-driven learning of physics models - The SINDy method - PDE-FIND - ROMs with SINDy
Friday 31 July 2020 ¶
14:00 Lecture/exercises 5 - Feliks Nüske (Paderborn University)
Lecture/exercises 5
- Feliks Nüske (Paderborn University)
14:00 - 15:30
Coarse Graining for SDEs - Effective Dynamics - Reduced Generator - Parameter Estimation
16:00 Lecture/exercises 5 - Benjamin Peherstorfer (New York University)
Lecture/exercises 5
- Benjamin Peherstorfer (New York University)
16:00 - 17:30
Multifidelity and using data-fit models together with traditional model for, e.g., uncertainty quantification
18:00 Lecture/exercises 5 - J. Nathan Kutz (University of Washington)
Lecture/exercises 5
- J. Nathan Kutz (University of Washington)
18:00 - 19:30
Learning coordinates and models - SINDy autoencoders - Koopman autoencoders
19:30 Closing
Closing
19:30 - 19:45

Choose timezone