ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
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7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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2026 7verdicts
UNVERDICTED 7representative citing papers
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.
Experiment-as-Code Labs encodes experiments as declarative configurations that AI agents generate, systems software analyzes and orchestrates, and device APIs execute on physical lab hardware.
An affordable Arduino-based IoT setup generates real-time optical data for students to compare traversal, Bayesian, and deep learning methods in a self-driving experimental workflow.
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
Infrastructure is the primary obstacle to embodied AI for science in the Global South, and addressing it turns automation into essential capacity rather than a luxury.
citing papers explorer
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ShardTensor: Domain Parallelism for Scientific Machine Learning
ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
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Rethinking Publication: A Certification Framework for AI-Enabled Research
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
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Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
Experiment-as-Code Labs encodes experiments as declarative configurations that AI agents generate, systems software analyzes and orchestrates, and device APIs execute on physical lab hardware.
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Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things
An affordable Arduino-based IoT setup generates real-time optical data for students to compare traversal, Bayesian, and deep learning methods in a self-driving experimental workflow.
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Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
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Infrastructure First: Enabling Embodied AI for Science in the Global South
Infrastructure is the primary obstacle to embodied AI for science in the Global South, and addressing it turns automation into essential capacity rather than a luxury.