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A physics-informed machine learning approach for predicting dynamic behavior of reacting flows with application to hydrogen jet flames
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An algorithm for physics informed scan path optimization in additive manufacturing
Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components:1. A heat transfer model for simulating the temperature field at a given time.2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt.3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this work, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.
Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems
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Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors
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Defending against cyber-attacks in building HVAC systems through energy performance evaluation using a physics-informed dynamic Bayesian network (PIDBN)
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Δ -PINNs: Physics-informed neural networks on complex geometries
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A physics-informed machine learning model for the prediction of drop breakup in two-phase flows
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Physics-informed deep learning for prediction of CO2 storage site response
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Thermodynamically consistent physics-informed neural networks for hyperbolic systems
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Long-time integration of parametric evolution equations with physics-informed DeepONets
Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.
Inferring vortex induced vibrations of flexible cylinders using physics-informed neural networks
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A physically-informed long short-term memory-based tool for predicting extensive droughts in the distant future
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Utilizing physics-informed synthetic data to predict reactor operations
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Probabilistic physics-informed machine learning for dynamic systems
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PND: Physics-informed neural-network software for molecular dynamics applications
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