Aug. 18, 2023, 3:28 a.m. | /u/ai_physics2023

Machine Learning www.reddit.com

Recently, two interesting papers trying to reconcile the classical methods, specifically, finite difference method with physics informed neural networks have been published that are worth reading.

1. [Weight initialization algorithm for physics-informed neural networks using finite differences](https://www.researchgate.net/publication/373118772_Weight_initialization_algorithm_for_physics-informed_neural_networks_using_finite_differences)
2. [Physics Informed Neural Network using Finite Difference Method](https://ieeexplore.ieee.org/document/9945171)

These two papers can be considered to be harmonizing classical finite difference method and Physics-Informed Neural Networks (PINNs).

The first paper incorporates finite difference solution for improving PINNs training loss. The second one uses …

difference machinelearning networks neural networks numerical paper physics physics-informed reading

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Senior Analytics Engineer (Retail)

@ Lightspeed Commerce | Toronto, Ontario, Canada

Data Scientist II, BIA GPS India Operations

@ Bristol Myers Squibb | Hyderabad

Analytics Engineer

@ Bestpass | Remote

Senior Analyst - Data Management

@ Marsh McLennan | Mumbai - Hiranandani