·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Meta’s AI agent Muse is now the No. 2 app in the US38m◆Schools are catching on to Big Tech’s playbook44m◆Anthropic reveals rogue AI agents hate CAPTCHAs, just like you2h◆India’s Pocket FM doubles revenue run rate to $500M as AI powers 93% of audio content2h◆How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules4h◆3 ways to prep for your next big race with Search4h◆Universal Music is launching an AI music platform with ElevenLabs4h◆Now everyone can put data to work5h◆Meta’s Muse AI works and creeps me out5h◆AI agents are flooding public services with new requests5h◆Maven Robotics wants to steal your robot deployment deal6h◆Why the current tech backlash feels different6h◆Mathematicians want proof OpenAI didn’t use their work9h◆Powering AI is an architecture problem9h◆Expanding AI access and cyber defense for federal, state, local, and tribal governments13h◆Introducing ChatGPT for Financial Services13h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty16h◆SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection16h◆WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic16h◆PAN: A World Model for General, Actionable, and Long-Horizon World Simulation16h◆Meta’s AI agent Muse is now the No. 2 app in the US38m◆Schools are catching on to Big Tech’s playbook44m◆Anthropic reveals rogue AI agents hate CAPTCHAs, just like you2h◆India’s Pocket FM doubles revenue run rate to $500M as AI powers 93% of audio content2h◆How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules4h◆3 ways to prep for your next big race with Search4h◆Universal Music is launching an AI music platform with ElevenLabs4h◆Now everyone can put data to work5h◆Meta’s Muse AI works and creeps me out5h◆AI agents are flooding public services with new requests5h◆Maven Robotics wants to steal your robot deployment deal6h◆Why the current tech backlash feels different6h◆Mathematicians want proof OpenAI didn’t use their work9h◆Powering AI is an architecture problem9h◆Expanding AI access and cyber defense for federal, state, local, and tribal governments13h◆Introducing ChatGPT for Financial Services13h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty16h◆SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection16h◆WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic16h◆PAN: A World Model for General, Actionable, and Long-Horizon World Simulation16h◆
News/Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
arxiv
PublishedJune 30, 2026 at 4:00 AM
▲bullish

Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.29440v1 Announce Type: new Abstract: Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-convex training. We introduce PCA--RaNN, a randomized latent neural operat

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
01
  • 01
    PCA--RaNN
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#numerical-analysis#optimization#uncertainty-quantification

No replies yet. Be first.

Mentioned models
01
  • 01
    PCA--RaNN
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#numerical-analysis#optimization#uncertainty-quantification

Related coverage

More from ARXIV
arxivPlanning and Scheduling Business Processes under Control-Flow Uncertainty16harxivSIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection16harxivWAPP: Safe Learning of Positive Security WAF Policies from Live Traffic16harxivPAN: A World Model for General, Actionable, and Long-Horizon World Simulation16h
The Bubble Brief
WEEKLY

Read machine-learning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews