{"ID":2861098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03413","arxiv_id":"2510.03413","title":"Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science","abstract":"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.","short_abstract":"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 towar...","url_abs":"https://arxiv.org/abs/2510.03413","url_pdf":"https://arxiv.org/pdf/2510.03413v2","authors":"[\"Lois Curfman McInnes\",\"Dorian Arnold\",\"Prasanna Balaprakash\",\"Mike Bernhardt\",\"Beth Cerny\",\"Anshu Dubey\",\"Roscoe Giles\",\"Denice Ward Hood\",\"Mary Ann Leung\",\"Vanessa Lopez-Marrero\",\"Paul Messina\",\"Olivia B. Newton\",\"Chris Oehmen\",\"Stefan M. Wild\",\"Jim Willenbring\",\"Lou Woodley\",\"Tony Baylis\",\"David E. Bernholdt\",\"Chris Camano\",\"Johannah Cohoon\",\"Charles Ferenbaugh\",\"Stephen M. Fiore\",\"Sandra Gesing\",\"Diego Gomez-Zara\",\"James Howison\",\"Tanzima Islam\",\"David Kepczynski\",\"Charles Lively\",\"Harshitha Menon\",\"Bronson Messer\",\"Marieme Ngom\",\"Umesh Paliath\",\"Michael E. Papka\",\"Irene Qualters\",\"Elaine M. Raybourn\",\"Katherine Riley\",\"Paulina Rodriguez\",\"Damian Rouson\",\"Michelle Schwalbe\",\"Sudip K. Seal\",\"Ozge Surer\",\"Valerie Taylor\",\"Lingfei Wu\"]","published":"2025-10-03T18:22:47Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}