SAMM 2020: Learning Models from Data

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A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks (in session "Posters 3")

A function space random feature model for PDE solution maps (in session "Posters 1")

A Hamiltonian Monte Carlo Bayesian Inference Approach Using Deep Learning for Modeling Metabolism (in session "Posters 2")

Adaptive Interpolatory MOR by Learning the Error Estimator in the Parameter Domain (in session "Posters 2")

An optimization-based approach for the reduction of parametrized conservation laws with discontinuities (in session "Posters 1")

Analysis of bubble dynamics using data-driven dynamical systems and machine learning (in session "Posters 1")

Analytical and Data-driven Models to Predict Algae Biofilm Growth in Water Treatment (in session "Posters 1")

Analytical and Learning Model of a Hybrid-Fluidic Elastomer Actuator for Reliable Control and Perturbation Detection (in session "Posters 1")

Analytical Modeling and Evaluation of Curvature-Dependent Distributed Friction Force in Tendon-Driven Continuum Manipulators (in session "Posters 1")

Analyzing the Transition to Buffeting of a 2D Airfoil using the Dynamic Mode Decomposition (in session "Posters 1")

Artificial neural network for bifurcating phenomena modelled by nonlinear parametrized PDEs (in session "Posters 2")

Basis Generation Techniques for Symplectic Model Order Reduction (in session "Posters 3")

Can machine learning methods be used to create parametrized reduced models of vibro-acoustic systems? (in session "Posters 3")

Chance-constrained optimal control of hyperbolic supply systems (in session "Posters 3")

Convolutional Neural Networks for object detection in professional appliances (in session "Posters 2")

Data-based Approach for Fault Diagnosis of Hydropower Rotors (in session "Posters 3")

Data-based soil-tool interaction force prediction based on measurements and the Discrete Element Method (in session "Posters 3")

Data-driven computational continuum mechanics (in session "Posters 3")

Data-Driven Identification and Reduction of Dynamical Systems with the Loewner Framework (in session "Posters 2")

Data-Driven Learning of Reduced-Order Dynamics for a Parametrized Shallow Water Equation (in session "Posters 3")

Data-driven metamodelling in Global Sensitivity Analysis (in session "Posters 2")

Data-driven Reduced Order Model of Flow-Induced Piezoelectric Energy Harvesters (in session "Posters 3")

Data-driven reduced-order modeling from noisy measurements: new results and future perspectives (in session "Posters 2")

Deep Kernel approaches with a Neural-Network-like structure (in session "Posters 3")

Deep learning based model reduction approaches in flow models (in session "Posters 3")

Deep learning of multibody minimal coordinates for estimation (in session "Posters 2")

Deep learning of superstructures in turbulence (in session "Posters 3")

Deep Neural Networks for Hyperbolic Conservation laws with Non-convex Flux (in session "Posters 2")

Discovering the governing PDE of an active nematic system from video data (in session "Posters 1")

Enhancing battery recharge performance by combined Machine Learning and PDE modelling (in session "Posters 3")

Extrapolating Nuclear Masses using Bayesian Gaussian Process Regression (in session "Posters 1")

Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy (in session "Posters 2")

Insights into squealing disk brakes through explainable machine learning for time series data (in session "Posters 3")

Kernel-based Active Subspaces with application to CFD problems using Discontinuous Galerkin method (in session "Posters 2")

Learning age-related chronic disease progression from cognitive measurements (in session "Posters 1")

Learning Constitutive Relations using Symmetric Positive Definite Neural Networks (in session "Posters 1")

Learning from Data for Traffic Control (in session "Posters 2")

Learning the Interfacial Area Equation from Data (in session "Posters 1")

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Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics (in session "Posters 3")

Machine learning for parameter identification and model reduction of gradient-enhanced damage models for metal forming processes (in session "Posters 2")

Machine learning for parameters identification in structural joints models (in session "Posters 1")

Model adaptation for hyperbolic balance laws employing constraint aware neural networks (in session "Posters 2")

Model Reduction for Advection Dominated Problems (in session "Posters 3")

Modulus-based iterative methods for constrained $\ell_p$-$\ell_q$ minimization (in session "Posters 1")

Multipopulation mortality rates modelling and forecasting: The multivariate functional principal component analysis approach (in session "Posters 2")

MUQ-hIPPYlib: A Bayesian Inference Software Framework Integrating Data with Complex Predictive Models under Uncertainty (in session "Posters 1")

Nonlinear model reduction for one-dimensional solidification process in additive manufacturing (in session "Posters 1")

One size does not fit all: Parameterized biomechanical models for crashworthiness simulations (in session "Posters 2")

Optimizing Intense Laser-Plasma Interactions with Evolutionary Algorithms and Machine Learning (in session "Posters 1")

PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain (in session "Posters 1")

Physics Guided Deep-Learning Based Nonlinear Reduced Order Model for Aeroelastic Applications (in session "Posters 3")

Prediction-Based Nature-Inspired Dynamic Optimization (in session "Posters 2")

Quantifying incompressible two-phase flow fields from the interface movement using physics-informed neural networks (in session "Posters 3")

Real-time virtual acoustics using physics-informed data-driven techniques (in session "Posters 2")

Reduced order modelling for data assimilation in parametrized optimal control framework (in session "Posters 2")

Sensor selection for hyper-parameterized linear Bayesian inverse problems (in session "Posters 2")

Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks (in session "Posters 1")

Stochastic frequency domain surrogate models for linear structural dynamics (in session "Posters 3")

Stochastic Grey-box Model of the Flow-Front Dynamics (in session "Posters 3")

System Identification by Sparse Bayesian Learning (in session "Posters 1")

The Construction and Application of Surrogate Models for Sensitivity Analysis (in session "Posters 3")

The use of machine learning in Computational Fluid Dynamics for an economic approach to flow optimization problems. (in session "Posters 3")

Towards Deep Learning Based Controllers with Nominal Closed Loop Stability Guarantees (in session "Posters 2")

Wavelet based dynamic mode decomposition (in session "Posters 2")

Weak SINDy: Galerkin-Based Data-Driven Model Selection (in session "Posters 1")

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7/27/20

7/28/20

7/29/20

7/30/20

7/31/20

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