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Latest from AI Agent Research Hub

32 recent articles

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

AI Agent Research Hub

Mar 13, 2026 · Artificial Intelligence

Deduction vs Induction: 152‑Page Review of Classical vs ML PDE Solvers

This extensive 152‑page review evaluates classical numerical solvers and machine‑learning approaches for partial differential equations using a unified six‑challenge framework, revealing that their fundamental difference lies in epistemology—deductive error bounds versus inductive statistical accuracy—and offering guidance on method choice, hybrid designs, and future research directions.

Computational ChallengesError CertificationHybrid Solvers

0 likes · 26 min read

Deduction vs Induction: 152‑Page Review of Classical vs ML PDE Solvers

AI Agent Research Hub

Mar 10, 2026 · Artificial Intelligence

How Knowledge Distillation Lets Neural Networks Grow Physical Symmetry Without Hard PINN Constraints

The paper introduces Ψ‑NN, a knowledge‑distillation framework that automatically discovers physics‑consistent network structures for PINNs, eliminating the need for manually imposed loss‑function constraints and achieving faster convergence, higher accuracy, and transferable architectures across PDE problems.

Knowledge DistillationNetwork Structure Discoveryhierarchical clustering

0 likes · 26 min read

How Knowledge Distillation Lets Neural Networks Grow Physical Symmetry Without Hard PINN Constraints

AI Agent Research Hub

Mar 9, 2026 · Artificial Intelligence

How Claude Code AI Agents Generated 100 Research Papers in 10 Days

Within 228 hours, the Fully Automated Research System (FARS) built on Claude Code and other AI agents used 160 NVIDIA GPUs to produce 100 peer‑review‑level papers, achieving an average ICLR score of 5.05—higher than human submissions—while highlighting the expanding role, limits, and safety concerns of AI‑driven scientific automation.

AI AgentsAI safetyClaude Code

0 likes · 31 min read

How Claude Code AI Agents Generated 100 Research Papers in 10 Days

AI Agent Research Hub

Mar 2, 2026 · Artificial Intelligence

How AI Agents Can Fully Automate Scientific Research and Boost Productivity

This article surveys the emerging AI‑agent ecosystem that automates the full research lifecycle—from data collection and cleaning to regression, literature synthesis and visualization—highlighting open‑source systems such as OpenScholar, Automated‑AI‑Researcher, AlphaEvolve and PaperBanana, their automation maturity, practical usage guides, known limitations, and essential human‑verification checkpoints.

AI AgentsClaude CodeOpenScholar

0 likes · 26 min read

How AI Agents Can Fully Automate Scientific Research and Boost Productivity

AI Agent Research Hub

Feb 26, 2026 · Artificial Intelligence

Can PINNs Reconstruct Velocity and Pressure Fields from Passive Scalar Visualizations?

This article analyzes the Science paper that uses physics‑informed neural networks (HFM) to infer complete velocity and pressure fields from only passive scalar concentration data such as smoke or dye, detailing the mathematical formulation, network architecture, training strategy, benchmark results, robustness studies, and the method’s limitations and broader impact.

Fluid MechanicsPINNsPassive Scalar

0 likes · 32 min read

Can PINNs Reconstruct Velocity and Pressure Fields from Passive Scalar Visualizations?

AI Agent Research Hub

Feb 24, 2026 · Artificial Intelligence

Why PINNs Training Fails: Diagnosing and Fixing Gradient Pathologies

The article explains that physics‑informed neural networks often stall because the PDE residual loss dominates the boundary‑condition loss, causing severe gradient imbalance, and presents two remedies—an adaptive loss‑weighting scheme and a modified fully‑connected architecture—that together can improve prediction accuracy by up to two orders of magnitude.

Deep LearningPDEPINNs

0 likes · 28 min read

Why PINNs Training Fails: Diagnosing and Fixing Gradient Pathologies

AI Agent Research Hub

Feb 22, 2026 · Artificial Intelligence

Roadmap for Physics‑Informed Machine Learning: Lessons from the 2021 Nature Review

This review of the 2021 Nature Reviews Physics article maps the emerging field of physics‑informed machine learning, outlines three bias pathways for embedding physics, compares PINNs, Neural Operators and other methods, discusses software ecosystems, practical guidelines, and future research directions.

DeepXDEPINNsUncertainty Quantification

0 likes · 38 min read

Roadmap for Physics‑Informed Machine Learning: Lessons from the 2021 Nature Review

AI Agent Research Hub

Feb 21, 2026 · Artificial Intelligence

Why Physics‑Informed Neural Networks (PINNs) Became a 20,000‑Citation Breakthrough

This article reviews the highly cited 2019 JCP paper that introduced Physics‑Informed Neural Networks, explains their core idea of embedding PDE residuals into the loss, compares them with contemporaneous methods, details implementation choices, showcases forward and inverse experiments, and discusses their impact, limitations, and future research directions.

Deep LearningPINNsScientific Computing

0 likes · 26 min read

Why Physics‑Informed Neural Networks (PINNs) Became a 20,000‑Citation Breakthrough

AI Agent Research Hub

Feb 19, 2026 · Artificial Intelligence

Why Claude Sonnet 4.6 Is My Most Powerful and Cost‑Effective AI Research Assistant

The article evaluates Anthropic's Claude Sonnet 4.6 as a comprehensive research assistant, detailing its performance on literature surveys, open‑source code analysis, algorithm implementation, cost savings, benchmark scores, and practical limitations across multiple scientific workflows.

AI research assistantClaude Sonnet 4.6Large Language Model

0 likes · 20 min read

Why Claude Sonnet 4.6 Is My Most Powerful and Cost‑Effective AI Research Assistant