Search Results
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
We use the projection basis vectors in the active subspace as well as the principal output subspace to construct the weights for the first and last layers of the neural network, respectively.
Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs
Open Government Data (OGD) is being published by various public administration organizations around the globe.
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave.
Y Chromosome of Aisin Gioro, the Imperial House of Qing Dynasty
We therefore conclude that this haplotype is the Y chromosome of the House of Aisin Gioro.
Populations and Evolution
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks.
Catala: A Programming Language for the Law
Law at large underpins modern society, codifying and governing many aspects of citizens' daily lives.
Programming Languages
Universal Differential Equations for Scientific Machine Learning
In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets."
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations.
