High-fidelity models (HFMs) of physical phenomena are frequently expressed using partial
differential equations which require expensive and complex numerical methods for solution …

Numerical solutions of partial differential equations (PDEs) require expensive simulations,
limiting their application in design optimization, model-based control, and large-scale …

Deep-learning-based flow emulators are used to predict the flowfield around parametrically
defined airfoils and then used in place of Reynolds-averaged Navier–Stokes solvers in …

View Video Presentation: https://doi. org/10.2514/6.2023-1678. vid Deep-learning-based
flow emulators are used to predict the flow field around parametrically-defined airfoils. These …

Simulations of complex physical systems are typically realized by discretizing partial
differential equations (PDEs) on unstructured meshes. While neural networks have recently …

Neural fields have become widely used in various fields, from shape representation to
neural rendering, and for solving partial differential equations (PDEs). With the advent of …

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

Neural fields have become widely used in various fields, from shape representation to
neural rendering, and for solving partial differential equations (PDEs). With the advent of …

Designing physical artifacts that serve a purpose---such as tools and other functional
structures---is central to engineering as well as everyday human behavior. Though …

We present a data-driven technique that can learn from physicalbased simulations for the
instant prediction of field distribution of three-dimension (3D) objects. Such techniques are …