Finite operator learning: Bridging neural operators and numerical methods for efficient parametric solution and optimization of pdes
We introduce a method that combines neural operators, physics-informed machine learning,
and standard numerical methods for solving PDEs. The proposed approach extends each of …
and standard numerical methods for solving PDEs. The proposed approach extends each of …
[HTML][HTML] Investigation of physics-informed deep learning for the prediction of parametric, three-dimensional flow based on boundary data
The placement of temperature sensitive and safety-critical components is crucial in the
automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that …
automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that …
Geometric improvement of the sheet/film extrusion die using Rabinowitsch-Mooney analysis
Abstract The well-known Winter-Fritz horseshoe die introduced in the 1980s has been used
in polymer sheet/film extrusion die and coating die industries with success. The geometric …
in polymer sheet/film extrusion die and coating die industries with success. The geometric …
A model hierarchy for predicting the flow in stirred tanks with physics-informed neural networks
This paper explores the potential of Physics-Informed Neural Networks (PINNs) to serve as
Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors …
Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors …
Shape Optimization of CAD-Compliant Boundary-Conforming Microstructured Geometries
JM Zwar - 2024 - repositum.tuwien.at
Recent developments in additive manufacturing have opened up a vast field of new design
possibilities that cannot be fully exploited by traditional engineering methods. These …
possibilities that cannot be fully exploited by traditional engineering methods. These …