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Found 12 papers, 1 papers with code
Tailoring Generative Adversarial Networks for Smooth Airfoil Design
In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils.
Generalizable Neural Physics Solvers by Baldwinian Evolution
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them.
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution.
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases.
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Hence, neuroevolution algorithms, with their superior global search capacity, may be a better choice for PINNs relative to gradient descent methods.
Design of Turing Systems with Physics-Informed Neural Networks
Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design.
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating physics-based domain knowledge into neural network models for many important real-world systems.
FastFlow: AI for Fast Urban Wind Velocity Prediction
Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e. g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations.
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy.
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
In this paper, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs.
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
In addition, we propose a novel transfer optimization scheme for use in such surrogate modeling scenarios and demonstrate an approximately 3x improvement in speed to convergence and an order of magnitude improvement in predictive performance over conventional Xavier initialization for training of new scenarios.
Can Transfer Neuroevolution Tractably Solve Your Differential Equations?
In the context of solving differential equations, we are faced with the problem of finding globally optimum parameters of the network, instead of being concerned with out-of-sample generalization.
