Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
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2025
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| author | McInnes, Lois Curfman Arnold, Dorian Balaprakash, Prasanna Bernhardt, Mike Cerny, Beth Dubey, Anshu Giles, Roscoe Hood, Denice Ward Leung, Mary Ann Lopez-Marrero, Vanessa Messina, Paul Newton, Olivia B. Oehmen, Chris Wild, Stefan M. Willenbring, Jim Woodley, Lou Baylis, Tony Bernholdt, David E. Camano, Chris Cohoon, Johannah Ferenbaugh, Charles Fiore, Stephen M. Gesing, Sandra Gomez-Zara, Diego Howison, James Islam, Tanzima Kepczynski, David Lively, Charles Menon, Harshitha Messer, Bronson Ngom, Marieme Paliath, Umesh Papka, Michael E. Qualters, Irene Raybourn, Elaine M. Riley, Katherine Rodriguez, Paulina Rouson, Damian Schwalbe, Michelle Seal, Sudip K. Surer, Ozge Taylor, Valerie Wu, Lingfei |
| author_facet | McInnes, Lois Curfman Arnold, Dorian Balaprakash, Prasanna Bernhardt, Mike Cerny, Beth Dubey, Anshu Giles, Roscoe Hood, Denice Ward Leung, Mary Ann Lopez-Marrero, Vanessa Messina, Paul Newton, Olivia B. Oehmen, Chris Wild, Stefan M. Willenbring, Jim Woodley, Lou Baylis, Tony Bernholdt, David E. Camano, Chris Cohoon, Johannah Ferenbaugh, Charles Fiore, Stephen M. Gesing, Sandra Gomez-Zara, Diego Howison, James Islam, Tanzima Kepczynski, David Lively, Charles Menon, Harshitha Messer, Bronson Ngom, Marieme Paliath, Umesh Papka, Michael E. Qualters, Irene Raybourn, Elaine M. Riley, Katherine Rodriguez, Paulina Rouson, Damian Schwalbe, Michelle Seal, Sudip K. Surer, Ozge Taylor, Valerie Wu, Lingfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03413 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science McInnes, Lois Curfman Arnold, Dorian Balaprakash, Prasanna Bernhardt, Mike Cerny, Beth Dubey, Anshu Giles, Roscoe Hood, Denice Ward Leung, Mary Ann Lopez-Marrero, Vanessa Messina, Paul Newton, Olivia B. Oehmen, Chris Wild, Stefan M. Willenbring, Jim Woodley, Lou Baylis, Tony Bernholdt, David E. Camano, Chris Cohoon, Johannah Ferenbaugh, Charles Fiore, Stephen M. Gesing, Sandra Gomez-Zara, Diego Howison, James Islam, Tanzima Kepczynski, David Lively, Charles Menon, Harshitha Messer, Bronson Ngom, Marieme Paliath, Umesh Papka, Michael E. Qualters, Irene Raybourn, Elaine M. Riley, Katherine Rodriguez, Paulina Rouson, Damian Schwalbe, Michelle Seal, Sudip K. Surer, Ozge Taylor, Valerie Wu, Lingfei Computational Engineering, Finance, and Science Artificial Intelligence 68T01, 68U01, 97M10 I.6.0; I.2.0; G.4; D.0 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. |
| title | Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence 68T01, 68U01, 97M10 I.6.0; I.2.0; G.4; D.0 |
| url | https://arxiv.org/abs/2510.03413 |