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[2602.22537] LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation
Summary
LUMOS introduces an innovative framework for scientific machine learning (SciML) that simplifies model design by integrating feature selection and parameter pruning through L0-regularized learning.
Why It Matters
As SciML continues to evolve, the need for accessible and efficient modeling tools becomes critical. LUMOS addresses this by reducing reliance on expert knowledge, making advanced modeling techniques more approachable for researchers across various fields.
Key Takeaways
- LUMOS combines feature selection and model pruning for improved SciML workflows.
- Achieves an average of 71.45% parameter reduction and 6.4x inference speedup.
- Utilizes semi-stochastic gating and reparameterization techniques for dynamic model adaptation.
- Demonstrated effectiveness across 13 diverse SciML workloads.
- Scalable training confirmed with Distributed Data Parallel on multiple GPUs.
Computer Science > Machine Learning arXiv:2602.22537 (cs) [Submitted on 26 Feb 2026] Title:LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation Authors:Shouwei Gao, Xu Zheng, Dongsheng Luo, Sheng Di, Wenqian Dong View a PDF of the paper titled LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation, by Shouwei Gao and 4 other authors View PDF Abstract:The rapid growth of scientific machine learning (SciML) has accelerated discovery across diverse domains, yet designing effective SciML models remains a challenging task. In practice, building such models often requires substantial prior knowledge and manual expertise, particularly in determining which input features to use and how large the model should be. We introduce LUMOS, an end-to-end framework based on L0-regularized learning that unifies feature selection and model pruning to democratize SciML model design. By employing semi-stochastic gating and reparameterization techniques, LUMOS dynamically selects informative features and prunes redundant parameters during training, reducing the reliance on manual tuning while maintaining predictive accuracy. We evaluate LUMOS across 13 diverse SciML workloads, including cosmology and molecular sciences, and demonstrate its effectiveness and generalizability. Experiments on 13 SciML models show that LUMOS achieves 71.45% parameter reduction and a 6.4x inference speedu...