discretization. The primary application of neural operators is in learning surrogate maps for the solution operators of partial differential equations (PDEs)...

discovery, scientific simulations and engineering design. She invented Neural Operators that extend deep learning to modeling multi-scale processes in these...

". Bhan, Shi, Krstic, Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping (2023). "Neural Operators for Bypassing Gain and...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

In machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical...

many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used...

the sample model neural network above. Since the Quantum neural network being discussed uses fan-out Unitary operators, and each operator only acts on its...

Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

Neural differential equations are a class of models in machine learning that combine neural networks with the mathematical framework of differential equations...

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term...

Neural tube defects (NTDs) are a group of birth defects in which an opening in the spine or cranium remains from early in human development. In the third...

have built devices to interface with neural cells and entire neural networks in vitro. Experiments on cultured neural tissue focused on building problem-solving...

with the translation operators. Consider the family S of operators consisting of all such convolutions and the translation operators. Then S is a commuting...

in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine...

The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution...

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional...

mapped to vectors of real numbers. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic...

macro-operators—i.e., searching for useful macro-operators to be learned from sequences of basic problem-solving actions. Good macro-operators simplify...

The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural-net machine designed by Marvin Lee Minsky. Prompted by a letter from Minsky,...

Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed...

This caused the field of neural network research to stagnate for many years, before it was recognised that a feedforward neural network with two or more...

is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture...

; Koepke, Kathleen Mann (2015). Development of Mathematical Cognition: Neural Substrates and Genetic Influences. Academic Press. ISBN 978-0-12-801909-2...

algorithms for reinforcement learning in neural networks". Proceedings of the IEEE First International Conference on Neural Networks. CiteSeerX 10.1.1.129.8871...

were token embeddings. ViTs were designed as alternatives to convolutional neural networks (CNNs) in computer vision applications. They have different inductive...

(1999). "Towards a High Performance Neural Branch Predictor" (PDF). Proceedings International Journal Conference on Neural Networks (IJCNN). Archived from...

and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to...