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Mixture fraction enhanced physics-informed neural networks for flow reconstruction in pool fires

Gonzalez Martinez, Pablo (author)

This thesis explores the use of physics-informed neural networks (PINNs) to reconstruct the flow fields in a pool fire flame, a canonical configuration in non-premixed combustion. Due to the difficulty in obtaining adequate experimental characterizations of such flows, reacting flows like pool fires stand in need of novel methods capable of...

master thesis 2024

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Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations

Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)

This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter...

journal article 2024

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Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

Liu, J. (author), Borja, Pablo (author), Della Santina, C. (author)

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics-informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model-based controllers originally developed with first...

journal article 2024

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Physics-Informed Neural Networks for Designing Spacecraft Trajectories

Goldman, Thomas (author)

This thesis proposes an unsupervised Physics-Informed Neural Network (PINN) for solving optimal control problems with the direct method to design and optimize transfer trajectories. The network adheres analytically to boundary conditions and includes the objective fitness as regularization in its loss function. A test scenario of a planar Earth...

master thesis 2023

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Modelling of coupled reservoir and wellbore for energy transition

Mansour Pour, K. (author)

Borehole operations play a crucial role in managing various subsurface activities related to energy, including energy storage, geothermal energy production, CO2 sequestration, oil and gas extraction, wastewater disposal, and thermal recovery processes. In recent times, intelligent well technologies, such as long deviated multi-lateral wells...

doctoral thesis 2023

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Physics-informed neural networks for highly compressible flows: assessing and enhancing shock-capturing capabilities

Wagenaar, Thomas (author)

While physics-informed neural networks have been shown to accurately solve a wide range of fluid dynamics problems, their effectivity on highly compressible flows is so far limited. In particular, they struggle with transonic and supersonic problems that involve discontinuities such as shocks. While there have been multiple efforts to alleviate...

master thesis 2023

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Neural-Network Based Thermal Modeling of Small Satellites: A First-Principles Approach

Bhat, Ullas (author)

The use of small satellites, enabled by the standardization of the CubeSat specifications and miniaturization in electronics, has seen a rapid increase in the past decades. The low-cost and short development time of these satellites has made them an attractive option for both commercial and academic applications, making space exploration more...

master thesis 2023

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On leveraging physical knowledge to augment neural network-based surrogate models for simulations

Kaniewski, Tadeusz (author)

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

master thesis 2023

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Physics-informed Data Augmentation for Human Radar Signatures

Focante, Edoardo (author)

In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to...

master thesis 2023

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Physics-Informed Neural Networks for Solving Forward and Inverse Problems in Complex Beam Systems

Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)

This article proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler–Bernoulli and Timoshenko theories, where the double beams are connected with a Winkler foundation. In particular, forward and inverse problems for the Euler–Bernoulli...

journal article 2023

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Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-based Proxy Modeling

Hadjisotiriou, George (author)

Compositional simulation is computationally intensive for high-fidelity models due to thermodynamic equilibrium relations and the coupling of flow, transport and mass transfer. In this report, two methods for accelerated compositional simulation are outlined and demonstrated for a gas vaporization problem. The first method uses a proxy model...

master thesis 2022

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Velocity Reconstruction in Pool Fires using Physics-Informed Machine Learning

de Boer, Dirk (author)

In many flow experiments it is complex to measure all flow states of interest, leading to the need for a method to retrieve unmeasured flow states from measured ones. This work focuses on Hidden Fluid Mechanics (HFM), which refers to a Physics-Informed Neural Network (PINN) able to incorporate the Navier-Stokes (NS) equations into the loss...

master thesis 2022

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Physics Informed Neural Net Model for the Parameter Estimation of EV Charger

Kiran, Vinay (author)

The impedance-based approach is promising to analyse the harmonic emission and stability of a converter-based system, e.g., an EV fast charging station. When extracting the accurate impedance model of an EV charger, the knowledge of the charger’s circuit and controller parameters is indispensable. However, EV chargers’ manufacturers do not...

master thesis 2022

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Solving Partial Differential Equations using Physics-Informed Neural Networks

Popa, Vlad (author)

In an attempt to find alternatives for solving partial differential equations (PDEs)<br/>with traditional numerical methods, a new field has emerged which incorporates<br/>the residual of a PDE into the loss function of an Artificial Neural Network. This<br/>method is called Physics-Informed Neural Network (PINN). In this thesis, we study dense...

bachelor thesis 2022

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Deep Statistical Solver for Distribution System State Estimation

Habib, Benjamin (author)

Whereas in the past, Distribution Systems played a passive role in connecting customers to electricity, Distribution System Operators (DSOs) will have to take in the future a more active role in monitoring and regulating the network to deal with the new behaviors and dynamics of the system brought by the energy transition. State Estimation, a...

master thesis 2022

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Topology Optimization and Physics-Informed Neural Networks for Metamaterial Optics Design

Everingham, Dylan (author)

The development of optical metamaterials in recent years has enabled the design of novel optical devices with exciting properties and applications ranging across many fields, including in scientific instrumentation for space missions. This in<br/>turn has led to demand for computational methods which can produce efficient device designs....

master thesis 2022

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Physics-Informed Deep Learning for Computational Fluid Flow Analysis: Coupling of physics-informed neural networks and autoencoders for aerodynamic flow predictions on variable geometries

Kakkar, Samarth (author)

The main objective of this thesis was to explore the capabilities of neural networks in terms of representing governing differential equations, primarily in the purview of fluid/aero dynamic flows. The governing differential equations were accommodated within the loss functions for training the neural networks, thereby making them 'physics...

master thesis 2022

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A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing

Salvati, Enrico (author), Tognan, Alessandro (author), Laurenti, L. (author), Pelegatti, Marco (author), De Bona, Francesco (author)

Defects in additively manufactured materials are one of the leading sources of uncertainty in mechanical fatigue. Fracture mechanics concepts are useful to evaluate their influence, nevertheless, these approaches cannot account for the real morphology of defects. Preliminary attempts to exploit a more comprehensive description of defects can...

journal article 2022

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Predicting traction return current in electric railway systems through physics-informed neural networks

Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)

This paper addresses the problem of determining the distribution of the return current in electric railway traction systems. The dynamics of traction return current are simulated in all three space dimensions by informing the neural networks with the Partial Differential Equations (PDEs) known as telegraph equations. In addition, this work...

conference paper 2022

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Numerical Approaches for Investigating Quasiconvexity in the Context of Morrey’s Conjecture

Voss, Jendrik (author), Martin, R.P. (author), Sander, Oliver (author), Kumar, Siddhant (author), Kochmann, Dennis M. (author), Neff, Patrizio (author)

Deciding whether a given function is quasiconvex is generally a difficult task. Here, we discuss a number of numerical approaches that can be used in the search for a counterexample to the quasiconvexity of a given function W. We will demonstrate these methods using the planar isotropic rank-one convex function Wmagic+(F)=λmaxλmin-logλmaxλmin...

journal article 2022