Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically
improve the performance of Large Language Models (LLMs), particularly when dealing with …

The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Diffusion models achieve state-of-the-art performance in various generation tasks. However,
their theoretical foundations fall far behind. This paper studies score approximation …

Neural operators can learn nonlinear mappings between function spaces and offer a new
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …

Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

It is widely known that neural networks (NNs) are universal approximators of continuous
functions. However, a less known but powerful result is that a NN with a single hidden layer …