PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (42)

[1]
Chiral rank-$k$ truncations for the multigrid preconditioner of Wilson fermions in lattice QCD
2025Travis Whyte, Andreas Stathopoulos et al.
[2]
Operator SVD with Neural Networks via Nested Low-Rank Approximation
2024J. J. Ryu, Xiangxiang Xu et al.
[3]
PyAMG: Algebraic Multigrid Solvers in Python
2022Nathan Bell, Luke N. Olson et al.
[4]
EigenGame: PCA as a Nash Equilibrium
2020Ian M. Gemp, B. McWilliams et al.
[5]
Regularized linear autoencoders recover the principal components, eventually
2020Xuchan Bao, James Lucas et al.
[6]
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
2020R. Oftadeh, Jiayi Shen et al.
[7]
An Analysis of SVD for Deep Rotation Estimation
2020J. Levinson, Carlos Esteves et al.
[8]
Learning Algebraic Multigrid Using Graph Neural Networks
2020Ilay Luz, M. Galun et al.
[9]
Backpropagation-Friendly Eigendecomposition
2019Wei Wang, Zheng Dang et al.
[10]
Learning to Optimize Multigrid PDE Solvers
2019D. Greenfeld, M. Galun et al.
[11]
Spectral Inference Networks: Unifying Deep and Spectral Learning
2018David Pfau, Stig Petersen et al.
[12]
Spectral Normalization for Generative Adversarial Networks
2018Takeru Miyato, Toshiki Kataoka et al.
[13]
SpectralNet: Spectral Clustering using Deep Neural Networks
2018Uri Shaham, Kelly P. Stanton et al.
[14]
Algebraic multigrid methods *
2016Jinchao Xu, L. Zikatanov
[15]
Gaussian Error Linear Units (GELUs)
2016Dan Hendrycks, Kevin Gimpel
[16]
Bootstrap AMG
2011A. Brandt, J. Brannick et al.
[17]
Matrix Computations
2011Andrzej Chrzeszczyk, Jan Kochanowski
[18]
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
2009N. Halko, P. Martinsson et al.
[19]
Nodal Auxiliary Space Preconditioning in H(curl) and H(div) Spaces
2007R. Hiptmair, Jinchao Xu
[20]
An Introduction to Algebraic Multigrid
2006R. Falgout

Showing 20 of 42 references

Founder's Pitch

"NeuraLSP provides an efficient neural preconditioning method for faster PDE solutions, promising significant speed improvements for scientific computing."

Scientific ComputingScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/28/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.