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OpenAI shares a field report on using AI agents like Codex and Claude Code to assist in scientific computing projects, showing significant acceleration in software development and maintenance while shifting researchers' roles to verification and orchestration.
A practical guide to Python for engineers and scientists, covering NumPy, SciPy, Matplotlib, and more with exercises and real-world applications.
The U.S. Department of Energy and Arcee AI announced Genesis-Science-1 (GS1), an open-weight AI model designed for scientific computing workflows with a governed execution system that preserves reproducible records. The project involves contributions from national laboratories and aims to assist researchers with tasks like HPC code modernization, simulation campaigns, and materials science.
Introduces PI-Splines, a structured spline-based architecture for physics-informed learning that parametrizes unknown fields with trainable B-spline coefficients, providing compact support, analytical derivatives, and strong boundary condition enforcement, demonstrated as a competitive alternative to neural network-based methods.
This Wired article examines Python's performance limitations in scientific computing and discusses Julia as a potential solution to the two-language problem, drawing historical parallels from Turing Award lectures.
The Well is an open-source collection of 15TB of physics simulation datasets spanning 16 domains, created by Flatiron Institute and 11 universities, designed to train PDE surrogate models, enabling researchers to replace expensive supercomputer simulations with neural networks.
Anthropic's Claude Science desktop application focuses on solving the digital grunt work in scientists' daily research, such as data integration and supercomputer scheduling, rather than pursuing a grand-narrative AI scientist. It lowers the barrier to scientific computing through a natural language interface.
GP_ELITE is a pure-Python library for genetic-programming based symbolic regression, enabling discovery of interpretable mathematical formulas from small experimental datasets. Version 0.2.0 introduces Levenberg–Marquardt constant fitting, multi-restart reliability, Pareto front output, and extrapolation mode.
This article examines the declining popularity of Fortran in scientific computing and proposes modernizing it with dependent types to address the shortage of skilled Fortran programmers.
Proposes KL-DNN, a scalable operator learning framework that uses Karhunen-Loève expansions to handle large-scale PDE problems, achieving lower errors and two-order-of-magnitude speedup over DeepONet on a 3D carbon storage problem.
A half-day tutorial at ISC High Performance 2026 on using compiler-assisted tools (FPChecker/LLVM) for floating-point error analysis and profiling in C/C++ scientific codes.
Introduces the ICML 2026 paper Functional Attention, which treats functions as first-class citizens and replaces softmax point-to-point similarity with structured linear operators. It addresses issues of discretization, resolution sensitivity, and high computational complexity in traditional Transformers when handling continuous functions. Achieves or surpasses SOTA in tasks like PDE solving and 3D segmentation, and exhibits strong OOD generalization.
This paper introduces the degeneracy distillery, a method that automatically detects and resolves degenerate parameter combinations in physical models by estimating and flattening the Fisher information matrix, reducing the simulation budget required for neural posterior estimation while providing physical insight.
President Trump orders a national initiative to build a quantum computer for important scientific calculations.
This paper provides approximation and generalization error estimates for multi-input neural operators measured in Sobolev norms, analyzing how multiple input functions with different domains and regularities affect error bounds, applicable to PDE and scientific computing problems.
An integrated AI academic skills package for Chinese researchers, including three scenarios: paper writing, academic Office document generation, and scientific computing. It can be used directly with Claude Code and Codex.
This paper argues that using FP8 tensor cores with Ozaki Scheme II can replace native FP64 hardware for high-performance scientific computing on AI-optimized GPUs like NVIDIA's B300, achieving full double-precision accuracy at much higher throughput. The authors present a Tensor-Memory Equilibrium model and show that emulated FP64 performance can exceed native FP64 by orders of magnitude across all workloads.
This article discusses how a 1955 computer experiment at Los Alamos National Laboratory revolutionized the understanding of chaos, likely referring to the Fermi-Pasta-Ulam-Tsingou problem.
This paper proposes a spatially correlated curriculum learning framework for Physics-Informed Neural Networks (PINNs) that improves training stability and solution accuracy by leveraging spatial correlations among subregions, addressing issues like high-dimensional non-convex loss landscapes and imbalanced multi-objective constraints.
Proposes Constraint-Aware Flow Matching, a novel end-to-end framework that aligns the model's learning dynamics with constrained sampling procedure, mitigating distributional shift from projection corrections for high-quality constrained generation.