BestHub
Sign in
Tagged articles
15 articles
Page 1 of 1
AI Agent Research Hub
AI Agent Research Hub
Apr 22, 2026 · Artificial Intelligence

Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds

This tutorial presents the Time‑Induced Neural Network (TINN) framework that overcomes the time‑entanglement issue of standard PINNs by introducing a dedicated time‑subnet with FiLM modulation, employs a Levenberg‑Marquardt optimizer for second‑order updates, and demonstrates a 1e‑6 relative error solution of the 1‑D viscous Burgers equation in just 371 seconds on an RTX 4090.

Burgers EquationFiLM ModulationJAX
0 likes · 21 min read
Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds
AI Agent Research Hub
AI Agent Research Hub
Apr 1, 2026 · Artificial Intelligence

Scale‑PINN Solves High‑Re Navier‑Stokes in 100 seconds, Cutting Error by 96 %

The tutorial introduces Scale‑PINN, which adds an evolutionary regularization term inspired by pseudo‑time stepping to the PINN loss, enabling a shared‑backbone network to solve the lid‑driven cavity Navier‑Stokes problem at Re = 7500 in about 100 seconds and reducing the relative velocity error by roughly 96 % compared with a standard PINN.

Evolutionary regularizationHigh Reynolds numberJAX
0 likes · 25 min read
Scale‑PINN Solves High‑Re Navier‑Stokes in 100 seconds, Cutting Error by 96 %
DeepHub IMBA
DeepHub IMBA
Mar 25, 2026 · Artificial Intelligence

TPU Architecture and Pallas Kernels: From Memory Hierarchy to FlashAttention

This article explains why TPU programming differs from GPU, describes the explicit HBM‑VMEM‑register data movement required on TPU, introduces the Pallas grid‑BlockSpec‑Ref model, and walks through four progressively more complex kernels—including element‑wise add, tiled dot product, fused RMSNorm with scratch memory, and a production‑grade FlashAttention implementation—showing how each kernel maps to the TPU memory hierarchy and leverages Pallas features such as input_output_aliases and PrefetchScalarGridSpec.

FlashAttentionJAXMemory Hierarchy
0 likes · 20 min read
TPU Architecture and Pallas Kernels: From Memory Hierarchy to FlashAttention
AI Agent Research Hub
AI Agent Research Hub
Mar 18, 2026 · Artificial Intelligence

Variable-Scaling PINN for 2D Navier‑Stokes: How Coordinate Rescaling Improves Stiff PDE Training

This tutorial explains how a simple coordinate scaling (VS‑PINN) reduces stiffness in physics‑informed neural networks, demonstrates its implementation in JAX for the 2D steady incompressible Navier‑Stokes cylinder‑flow benchmark, and shows that after 80 000 Adam iterations the relative errors drop to 2.10 % (u), 5.06 % (v) and 4.45 % (p).

JAXNavier-StokesPINN
0 likes · 24 min read
Variable-Scaling PINN for 2D Navier‑Stokes: How Coordinate Rescaling Improves Stiff PDE Training
AI Agent Research Hub
AI Agent Research Hub
Mar 16, 2026 · Artificial Intelligence

How NTK Adaptive Weighting and Multi‑Scale Fourier Features Enable PINNs to Solve High‑Frequency PDEs

This tutorial explains why standard physics‑informed neural networks fail on high‑frequency partial differential equations due to spectral bias, and demonstrates how random Fourier feature embeddings, multi‑scale concatenation or spatio‑temporal separation, and Neural Tangent Kernel‑based adaptive loss weighting together overcome the bias and achieve accurate, stable solutions for heat, Poisson, and wave equations using JAX.

Fourier FeaturesJAXMulti-Scale
0 likes · 23 min read
How NTK Adaptive Weighting and Multi‑Scale Fourier Features Enable PINNs to Solve High‑Frequency PDEs
AI Agent Research Hub
AI Agent Research Hub
Mar 14, 2026 · Artificial Intelligence

Adaptive-Weight NTK-PINN Solves High-Frequency Wave Equation Using JAX

This tutorial explains how the Neural Tangent Kernel (NTK) perspective reveals the loss‑balance problem in Physics‑Informed Neural Networks (PINNs), introduces an NTK‑based adaptive‑weight algorithm, provides a full JAX implementation for a 1‑D high‑frequency wave equation, and shows that input normalisation dramatically improves accuracy while only modestly increasing training time.

JAXNTKPINN
0 likes · 27 min read
Adaptive-Weight NTK-PINN Solves High-Frequency Wave Equation Using JAX
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Dec 29, 2025 · Artificial Intelligence

How Brin’s Return Powers Google’s First ‘Sword’: The TPU Hardware Revolution

The article examines Google’s AI resurgence after Sergey Brin’s comeback, detailing the evolution of TPU hardware from v1 to v7, the strategic focus on algorithmic efficiency, comparisons with Nvidia’s B200, the role of JAX/XLA, and how these advances create a powerful competitive moat for Google’s AI infrastructure.

AI hardwareGoogle TPUJAX
0 likes · 8 min read
How Brin’s Return Powers Google’s First ‘Sword’: The TPU Hardware Revolution
AntTech
AntTech
Nov 4, 2025 · Artificial Intelligence

Unlock Native TPU Inference with SGLang-Jax: A Jax‑Powered Open‑Source Engine

SGLang-Jax is a cutting‑edge, fully Jax‑based open‑source inference engine that delivers native TPU performance, integrates advanced features like continuous batching, tensor and expert parallelism, and speculative decoding, while providing detailed installation and usage guidance for developers.

JAXSGLang-JaxTPU inference
0 likes · 10 min read
Unlock Native TPU Inference with SGLang-Jax: A Jax‑Powered Open‑Source Engine
Data Party THU
Data Party THU
Oct 20, 2025 · Artificial Intelligence

Fine-Tuning LLMs on TPU with Tunix: A Step‑by‑Step QLoRA Guide

This article introduces Google’s Tunix library for JAX‑based LLM post‑training, explains its core features such as supervised fine‑tuning, reinforcement learning and knowledge distillation, and provides detailed installation steps and a complete TPU‑accelerated QLoRA fine‑tuning workflow on the Gemma 2B model, including code snippets and inference testing.

Fine-tuningJAXLLM
0 likes · 8 min read
Fine-Tuning LLMs on TPU with Tunix: A Step‑by‑Step QLoRA Guide
21CTO
21CTO
Mar 18, 2024 · Artificial Intelligence

Inside Grok-1: Elon Musk’s Open‑Source 314B LLM Architecture Revealed

Elon Musk’s AI startup xAI has open‑sourced its 314‑billion‑parameter Grok‑1 model, detailing its Rust‑based, JAX‑powered architecture, extensive parameter count, training data limits, licensing terms, hardware requirements, and community reactions, offering developers unprecedented access to a competitive large‑language‑model framework.

Grok-1JAXModel architecture
0 likes · 9 min read
Inside Grok-1: Elon Musk’s Open‑Source 314B LLM Architecture Revealed
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 23, 2024 · Artificial Intelligence

Google’s Open‑Source Gemma Large Language Model: Architecture, Performance, and Community Reception

Google has released the open‑source Gemma LLM series (2B and 7B parameters) built on Gemini‑style architecture, offering free, commercial‑ready models that run on notebooks, support JAX/PyTorch/TensorFlow, outperform many open‑source peers, and have quickly sparked extensive community testing and discussion.

Artificial IntelligenceGemmaGoogle
0 likes · 5 min read
Google’s Open‑Source Gemma Large Language Model: Architecture, Performance, and Community Reception
DaTaobao Tech
DaTaobao Tech
Jul 15, 2022 · Artificial Intelligence

Edge AI Model Evaluation and Optimization with TensorFlow, JAX, and TVM

The article demonstrates how to evaluate, compress, and convert deep‑learning models for edge devices using TensorFlow, JAX, and TVM—showing a faster iPhone‑based MNIST training benchmark, FLOPs measurement scripts, TFLite/ONNX/CoreML conversion, TVM compilation with auto‑tuning, and up to 50 % speed improvements on mobile NPU hardware.

JAXTVMTensorFlow
0 likes · 29 min read
Edge AI Model Evaluation and Optimization with TensorFlow, JAX, and TVM
Alibaba Terminal Technology
Alibaba Terminal Technology
Jun 22, 2022 · Artificial Intelligence

How Fast Can Your Smartphone Run ML Models? Exploring Edge AI Optimization

This article examines the computational capabilities of modern mobile devices for machine learning, compares training times on a MacBook and iPhone, explains model evaluation metrics like FLOPs, and provides step‑by‑step guides for converting and optimizing models using TensorFlow, PyTorch, ONNX, JAX, and TVM for edge deployment.

JAXModel OptimizationTVM
0 likes · 29 min read
How Fast Can Your Smartphone Run ML Models? Exploring Edge AI Optimization