Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Not on the map. Why?
Abstract (arXiv)
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design--the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 65 references that are in the map. First posted on arXiv 3 Oct 2025.
Builds on
12 of 65 references.
References · 12
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
- 2025LlamaFirewall: An open source guardrail system for building secure AI agentscited by 127
- 2024Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equationscited by 91
- 2025Robin: A multi-agent system for automating scientific discoverycited by 62
- 2025LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilitiescited by 41
- 2016Research and Education in Computational Science and Engineeringcited by 2
- 2023A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainabilitycited by 2
- 2025Shock with Confidence: Formal Proofs of Correctness for Hyperbolic Partial Differential Equation Solverscited by 2
- 2018Talk to Me: A Case Study on Coordinating Expertise in Large-Scale Scientific Software Projectscited by 1
- 2018Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustainedcited by 1
- 2023Report of the DOE/NSF Workshop on Correctness in Scientific Computing, June 2023, Orlando, FLcited by 1
- 2024Toward a Cohesive AI and Simulation Software Ecosystem for Scientific Innovationcited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.