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Found 5 papers, 0 papers with code
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across different domains.
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
The proposed method, named Diffusion-inspired Temporal Transformer Operator (DiTTO), is inspired by latent diffusion models and their conditioning mechanism, which we use to incorporate the temporal evolution of the PDE, in combination with elements from the transformer architecture to improve its capabilities.
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO), for time-dependent forward modelling in groundwater systems.
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs).
A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem.
