#### Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs)

Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs) By Dania Sana Code: • See the complete report ...

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#### Derivation of the pressure function

Derivation of the pressure function By Veronika Riedl Code: Files to run: nocircle.m, onecircle.m or twocircles.m 1 Introduction This post ...

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#### Sheep Herding Game

Author: Daniël Veldman, FAU DCN-AvH Code: A sheep herding game in MATLAB developed for the Long Night of Science #NdW22 ...

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#### Lloyd’s Algorithm

Author: Martín Hernández, FAU DCN-AvH Code: In this repository, we show a code for Lloyd's algorithm. Also called Voronoid iteration, ...

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#### Using the support function for optimal shape design

Using the support function for optimal shape design By Ilias Ftouhi 1 Motivation Led by problems of optimal placement and ...

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#### Uniform Turnpike Property

Uniform Turnpike Property By Martín Hernández Salinas 1 Introduction In this post, we analyze a 1 − d heat equation ...

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#### The interplay of control and deep learning

Author: Borjan Geshkovski, MIT The interplay of control and Deep Learning By Borjan Geshkovski It is superﬂuous to state the ...

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#### Nonlinear hyperbolic systems: Modeling, controllability and applications

Nonlinear hyperbolic systems: Modeling, controllabiliy and applications By Yue Wang The control theory of hyperbolic systems is an important topic ...

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#### Kinetic theory of Bose Einstein Condensates

Kinetic theory of Bose Einstein Condensates By Minh-Binh Tran If a dilute gas of bosons, about one-hundred-thousandth the density of ...

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#### pyGasControls library (simulation software)

Author: Martin Gugat, Enrique Zuazua, Aleksey Sikstel, FAU DCN-AvH Code: [HINT] To run the software on your computer, you ...

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#### Hamilton-Jacobi Equations: Inverse Design

Author: Carlos Esteve, Deusto CCM Code: In a previous post "Inverse Design For Hamilton-Jacobi Equations", described all the possible initial ...

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#### Random Batch Methods for Linear-Quadratic Optimal Control Problems

Author: Daniel Veldman, FAU DCN-AvH Code: || Also available @Daniël's GitHub In a previous post "Randomized time-splitting in linear-quadratic optimal ...

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#### Augmented Lagragian preconditioners for incompressible flow

Author: Alexei Gazca, FAU DCN-AvH Code: Below is a description of the types of problems that can be tackled ...

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#### Transition Layers in Elliptic Equations

Transition Layers in Elliptic Equations By Maicon Sônego Stable transition layers in an unbalanced bistable equation Consider the following semi-linear ...

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#### Randomized time-splitting in linear-quadratic optimal control

Randomized time-splitting in linear-quadratic optimal control By Daniël Veldman Introduction Solving an optimal control problem for a large-scale dynamical system ...

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#### Felix Klein: A Legacy of Innovation in Mathematics and Education

Felix Klein: A Legacy of Innovation in Mathematics and Education By Roberto Rodríguez del Río, Complutense University of Madrid | ...

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#### Control of Advection-Diffusion Equations on Networks and Singular Limits

Control of Advection-Diffusion Equations on Networks and Singular Limits By Jon Asier Bárcena-Petisco, Márcio Cavalcante, Giuseppe Maria Coclite, Nicola de ...

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#### Probabilistic Constrained Optimization on Flow Networks

Probabilistic Constrained Optimization on Flow Networks By Michael Schuster This research was funded by DFG in the SFB Transregio 154: ...

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#### Perceptrons, Neural Networks and Dynamical Systems

Perceptrons, Neural Networks and Dynamical Systems By Sergi Andreu // This post is last part of the "Deep Learning and ...

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#### Deep Learning and Paradigms

Deep Learning and Paradigms By Sergi Andreu // This post is the 2nd. part of the "Opening the black box ...

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#### Opening the black box of Deep Learning

Opening the black box of Deep Learning By Sergi Andreu Deep Learning is one of the three main paradigms of ...

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#### Averaged dynamics and control for heat equations with random diffusion

Averaged dynamics and control for heat equations with random diffusion By Jon Asier Bárcena Petisco, Enrique Zuazua Background and motivation ...

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#### pyGasControls Framework

pyGasControls Framework By Martin Gugat, Enrique Zuazua, Aleksey Sikstel In order to optimize the operation of gas transportation networks, as ...

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#### Model-based optimization of ripening processes with feedback modules

Model-based optimization of ripening processes with feedback modules By Michele Spinola 1 Important remark This contribution presents a proof of ...

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#### Gas networks uncertainty and Probust constraints: model, distribution and optimization

Gas networks uncertainty and Probust constraints: model, distribution and optimization By Martin Gugat Gas transport and distribution systems are usually ...

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#### Q-learning for finite-dimensional problems

Q-learning for finite-dimensional problems By Carlos Esteve Reinforcement Learning Reinforcement Learning (RL) is, together with Supervised Learning and Unsupervised Learning, ...

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#### The interplay of control and deep learning

The interplay of control and Deep Learning By Borjan Geshkovski It is superﬂuous to state the impact deep (machine) learning ...

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#### Neural networks and Machine Learning

Neural networks and Machine Learning By Marius Yamakou Neural Networks with time delayed connections Neurons communicate with each other through ...

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#### Stochastic Synchronization of Chaotic Neurons

Stochastic Synchronization of Chaotic Neurons By Marius Yamakou Real biological neurons can show chaotic dynamics when excited by the certain ...

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#### Nonlocal population balance equations and applications

Nonlocal population balance equations and applications By Michele Spinola Motivational example: look ahead behavior of car drivers When analyzing traffic ...

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#### Inverse Design for Hamilton-Jacobi Equations

Inverse Design For Hamilton-Jacobi Equations By Carlos Esteve, Enrique Zuazua In many evolution models, the reconstruction of the initial state ...

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#### Stochastic Neural Dynamics

Stochastic Neural Dynamics By Marius Yamakou Neural activity shows fluctuations and unpredictable transitions in its dynamics. This randomness can be ...

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#### Controllability properties of fractional PDE

Controllability properties of fractional PDE By Umberto Biccari Controllability of the fractional heat equation Let \omega\subset (-1,1) be an open ...

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#### Flows on Networks

Flows on Networks By Enrique Zuazua, Nicola de Nitti PDE models on Networks In the last few decades, models based ...

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#### Stochastic optimization for simultaneous control

Stochastic optimization for simultaneous control By Umberto Biccari What is a simultaneous control problem? Consider the following parameter-dependent linear control ...

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#### Convexity and Starshapedness of feasible sets in Stationary Flow Networks

Convexity and Starshapedness of feasible sets in Stationary Flow Networks By Martin Gugat, Michael Schuster This research was funded by ...

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#### Collective dynamics modelling, Control and Simulation

Collective dynamics modelling, Control and Simulation By Dongnam Ko Collective dynamics Herds, packs, bird ﬂocks, and ﬁsh schools are common ...

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#### Data-Driven COVID Modeling

Classical models By Cyprien Neverov Compartmental epidemiological models [1] where introduced almost a century ago and are still considered the ...

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#### Non-local population balance equations

Non-local population balance equations By Michele Spinola Nichtlokale Populationsbilanzgleichungen. Der Verlauf des Weges wie zur Schule oder zur Arbeit hängt ...

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