·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.1h◆Conversational Gemini navigation comes to Apple CarPlay2h◆Granite Speech 5.0: Transcribe, Translate, and Attribute more Accurately and Faster2h◆AI promises productivity, but for managers, it’s mostly a headache for now3h◆OpenAI says an AI agent lied about disabling its safety monitoring system3h◆ChatGPT Ads expands to 26 new countries as over 100,000 advertisers have used the platform4h◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability4h◆OpenAI models are coming to Google Cloud5h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories6h◆How we built ChatGPT’s novel ad stack for user trust and relevance10h◆Patch the Planet: global response coordination as agents uncover critical vulnerabilities10h◆Using thousands of AI agents to uncover new leads in biological data12h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response13h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making13h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents13h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD13h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value13h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models13h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations13h◆When Synthetic Speech Is All You Have: Better Call GRPO13h◆Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.1h◆Conversational Gemini navigation comes to Apple CarPlay2h◆Granite Speech 5.0: Transcribe, Translate, and Attribute more Accurately and Faster2h◆AI promises productivity, but for managers, it’s mostly a headache for now3h◆OpenAI says an AI agent lied about disabling its safety monitoring system3h◆ChatGPT Ads expands to 26 new countries as over 100,000 advertisers have used the platform4h◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability4h◆OpenAI models are coming to Google Cloud5h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories6h◆How we built ChatGPT’s novel ad stack for user trust and relevance10h◆Patch the Planet: global response coordination as agents uncover critical vulnerabilities10h◆Using thousands of AI agents to uncover new leads in biological data12h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response13h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making13h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents13h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD13h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value13h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models13h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations13h◆When Synthetic Speech Is All You Have: Better Call GRPO13h◆
News/Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment
arxiv
PublishedJune 24, 2026 at 4:00 AM
—neutral

Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment

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

arXiv:2606.24851v1 Announce Type: new Abstract: Fourier Neural Operators (FNO) learn solution operators of partial differential equations by parameterizing global convolutions in the complex Fourier domain. For real-valued PDE solutions, the complex FFT carries representational redundancy through co

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
02
  • 01
    Fourier Neural Operator (FNO)
  • 02
    Hartley Neural Operator (HNO)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#neural operators#partial differential equations#spectral basis

No replies yet. Be first.

Mentioned models
02
  • 01
    Fourier Neural Operator (FNO)
  • 02
    Hartley Neural Operator (HNO)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#neural operators#partial differential equations#spectral basis

Related coverage

More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response13harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making13harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents13harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD13h
The Bubble Brief
MON · WED · FRI

Read machine learning insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews