The Forgotten Foundational Papers Behind PINNs

This article reviews the 1994 Dissanayake & Phan‑Thien and 1998 Lagaris et al. papers that first introduced feed‑forward neural networks as continuous trial functions for PDEs, contrasting their soft‑penalty and hard‑encoding boundary treatments and showing how they prefigure modern physics‑informed neural networks.

PINNsautomatic differentiationhard boundary encoding

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The Forgotten Foundational Papers Behind PINNs

DaTaobao Tech

Aug 9, 2022 · Artificial Intelligence

Differentiable Programming: Theory, Function Fitting, and Practical Implementations

Differentiable programming augments traditional code with automatic differentiation, enabling gradient‑descent optimization of scientific and UI functions; the article surveys its theory, demonstrates fitting a damping curve via logistic and polynomial models in Julia, Swift, and TensorFlow, and discusses trade‑offs between analytical interpretability and neural‑network flexibility.

Differentiable ProgrammingJavaScriptTensorFlow

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Differentiable Programming: Theory, Function Fitting, and Practical Implementations

Code DAO

May 8, 2022 · Artificial Intelligence

Solving Differential Equations with Physics‑Informed Neural Networks in PyTorch

This article explains how to build a Physics‑Informed Neural Network (PINN) in PyTorch to solve a simple logistic ordinary differential equation, covering the underlying theory, loss formulation with equation residuals and boundary conditions, network architecture, automatic differentiation, and training results.

PINNPhysics‑Informed Neural NetworksPyTorch

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Solving Differential Equations with Physics‑Informed Neural Networks in PyTorch

360 Smart Cloud

Sep 30, 2021 · Artificial Intelligence

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

This article explains how computational graphs can represent arbitrary neural networks, describes forward and reverse propagation, details the implementation of automatic differentiation with Python and NumPy, and demonstrates building and training a multilayer fully‑connected network on the MNIST dataset using custom graph nodes and optimizers.

Computational GraphDeep LearningNeural Networks

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Understanding Computational Graphs and Automatic Differentiation for Neural Networks

Python Programming Learning Circle

Aug 27, 2021 · Artificial Intelligence

An Introduction to JAX: Features, Installation, and Comparison with TensorFlow and PyTorch

This article introduces Google’s JAX library, covering its origins, core features such as automatic differentiation, JIT compilation, parallel and vectorized mapping, installation steps, code examples, and a comparative overview with TensorFlow and PyTorch for deep‑learning practitioners.

Deep LearningGPUJAX

0 likes · 11 min read

An Introduction to JAX: Features, Installation, and Comparison with TensorFlow and PyTorch