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Latest from AI Agent Research Hub

32 recent articles

AI Agent Research Hub

Apr 12, 2026 · Artificial Intelligence

FactReview: An AI‑Agent System for Evidence‑Grounded Peer Review of Papers and Code

FactReview redefines peer review by formalizing it as evidence‑grounded claim assessment, extracting structured statements from papers, locating related literature, and verifying empirical claims through sandboxed code execution, producing a five‑level label report; experiments on CompGCN and backend LLM analyses demonstrate its strengths and current limitations.

AI peer reviewLLMclaim verification

0 likes · 25 min read

FactReview: An AI‑Agent System for Evidence‑Grounded Peer Review of Papers and Code

AI Agent Research Hub

Apr 2, 2026 · Artificial Intelligence

Constrained Symbolic Regression and Weak Form Uncover Laws from Noisy Incomplete Data

By integrating universal physical symmetries, weak‑form integral transformations, and sparse symbolic regression, the authors devise a hybrid framework that extracts governing Navier‑Stokes equations from high‑dimensional, noisy, and partially observed fluid experiments, while also reconstructing hidden pressure and Lorentz force fields.

Navier-Stokesfluid dynamicslatent variables

0 likes · 12 min read

Constrained Symbolic Regression and Weak Form Uncover Laws from Noisy Incomplete Data

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 %

AI Agent Research Hub

Mar 31, 2026 · Artificial Intelligence

Why Do Physics-Informed Neural Networks Often Fail? Try Gradient‑Adaptive Weighted PINNs

Training Physics‑Informed Neural Networks (PINNs) often stalls due to gradient‑flow pathology, but a gradient‑adaptive weighting scheme dramatically rebalances loss contributions, as demonstrated on a nonlinear Klein‑Gordon equation where error drops by 95.5% compared with standard PINNs.

JAXKlein-GordonPINN

0 likes · 24 min read

Why Do Physics-Informed Neural Networks Often Fail? Try Gradient‑Adaptive Weighted PINNs

AI Agent Research Hub

Mar 25, 2026 · Industry Insights

2026 Guide to Choosing Journals for Physics-Informed Neural Networks (PINNs)

This article provides a concise reference of 21 common PINN journals, organized by region and TOP status, and offers practical advice on matching your research to the right audience rather than chasing journal rankings.

Artificial IntelligenceJournal SelectionPINNs

0 likes · 11 min read

2026 Guide to Choosing Journals for Physics-Informed Neural Networks (PINNs)

AI Agent Research Hub

Mar 24, 2026 · Artificial Intelligence

How PeRCNN Turns Convolution Kernels into Differential Operators for Physics‑Informed Learning

PeRCNN embeds physics directly into its architecture by replacing additive nonlinearities with element‑wise multiplication in Π‑blocks, enabling convolution kernels to act as finite‑difference operators, which yields superior forward and inverse PDE solving, accurate coefficient identification, robust equation discovery, and interpretable models, as demonstrated on multiple reaction‑diffusion benchmarks.

Deep LearningPeRCNNconvolutional neural network

0 likes · 22 min read

How PeRCNN Turns Convolution Kernels into Differential Operators for Physics‑Informed Learning

AI Agent Research Hub

Mar 20, 2026 · Artificial Intelligence

Spectral Division of Labor: How HINTS Blends Jacobi and DeepONet for Uniform PDE Convergence

The HINTS framework exploits the complementary spectral biases of classical Jacobi/Gauss‑Seidel relaxations and DeepONet neural operators, alternating them at a fixed ratio to achieve fast, uniform convergence for both positive‑definite and indefinite PDE systems, and integrates seamlessly with multigrid and Krylov solvers.

DeepONetHybrid Iterative MethodsJacobi

0 likes · 27 min read

Spectral Division of Labor: How HINTS Blends Jacobi and DeepONet for Uniform PDE Convergence

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

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

Mar 15, 2026 · Artificial Intelligence

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

0 likes · 22 min read

The Forgotten Foundational Papers Behind PINNs