Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2608.26519

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Lois Curfman McInnes·Dorian Arnold·Prasanna Balaprakash·Mike Bernhardt·Franck Cappello·Beth Cerny·Deborah DiazGranados·Anshu Dubey·Nichole Etienne·Roscoe Giles·Diego Gomez-Zara·Denice Ward Hood·Mary Ann Leung·Vanessa Lopez-Marrero·Olivia B. Newton·Irene Qualters·Keita Teranishi·Stefan M. Wild·Gabrielle Allen·Richard Arthur·Alexandra Ballow·Tony Baylis·David E. Bernholdt·Daniel Bielich·Johanna Cohoon·Jeremy Crampton·Charles Ferenbaugh·Stephen M. Fiore·Thomas Herault·Tanzima Islam·Stephen Jacobsohn·Meifeng Lin·Charles Lively·Satoshi Matsuoka·Stasa Milojevic·Daniel Nichols·Chris Oehmen·Santiago Ospina Tabares·Michael E. Papka·Katherine Riley·Damian Rouson·Sudip K. Seal·Brittany Segundo·John Shalf·Andrew Siegel·Valerie Taylor·Jim Willenbring·Lou Woodley
View original source ↗Read source document ↗Export metadata ↓

Abstract

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

Explore related subjects

cs.CEcs.SE

Keep this discovery

BibTeXRIS

Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash, Mike Bernhardt, Franck Cappello, Beth Cerny, Deborah DiazGranados, Anshu Dubey, Nichole Etienne, Roscoe Giles, Diego Gomez-Zara, Denice Ward Hood, Mary Ann Leung, Vanessa Lopez-Marrero, Olivia B. Newton, Irene Qualters, Keita Teranishi, Stefan M. Wild, Gabrielle Allen, Richard Arthur, Alexandra Ballow, Tony Baylis, David E. Bernholdt, Daniel Bielich, Johanna Cohoon, Jeremy Crampton, Charles Ferenbaugh, Stephen M. Fiore, Thomas Herault, Tanzima Islam, Stephen Jacobsohn, Meifeng Lin, Charles Lively, Satoshi Matsuoka, Stasa Milojevic, Daniel Nichols, Chris Oehmen, Santiago Ospina Tabares, Michael E. Papka, Katherine Riley, Damian Rouson, Sudip K. Seal, Brittany Segundo, John Shalf, Andrew Siegel, Valerie Taylor, Jim Willenbring, Lou Woodley. 2026-08-28. Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science. https://arxiv.org/abs/2608.26519

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.CE subjectcs.SE subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

When Literature Data Mislead Artificial Intelligence in Materials Discovery

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

cs.IR↗
arXiv2026

Intrinsic Finite Element Methods for Fluids on Riemannian Manifolds Compared with Surface FEM

We present an intrinsic finite element formulation for the incompressible Navier--Stokes equations on Riemannian manifolds. We derive the corresponding weak formulation and prove that the backward Euler discretisation is energy stable. The proposed framework is validated on several representative manifolds, with particular attention paid to the long-time behaviour of the flow and its convergence to steady-state solutions represented by Killing vector fields. Comprehensive comparisons are performed with the surface finite element method and a corresponding eigenvalue formulation for Killing vector fields. The numerical results demonstrate that the intrinsic formulation provides an accurate, computationally efficient, and geometrically transparent alternative to embedded surface finite element formulations, while naturally extending to higher-dimensional Riemannian manifolds.

cs.CE↗