A Power-Efficient Monte Carlo Framework for Nonlinear Light-Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering

A Power-Efficient Monte Carlo Framework for Nonlinear Light-Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 September 2025 V1 Latest version Share on A Power-Efficient Monte Carlo Framework for Nonlinear Light-Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering Authors : Ilya Vladyko [email protected] , Vladislav Yakovlev 0000-0002-4557-1013 , and A. Doronin 0000-0003-4615-9220 Authors Info & Affiliations https://doi.org/10.22541/au.175766965.59561322/v1 180 views 183 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract We present a power-efficient Monte Carlo simulation framework for modeling nonlinear light–matter interactions, with a particular focus on spontaneous and stimulated Raman scattering in turbid biological media. By leveraging Apple’s unified memory architecture and implementing a highly parallelized photon transport kernel using the Metal Shading Language, our method achieves substantial gains in computational efficiency without compromising accuracy. The algorithm incorporates a probabilistic treatment of nonlinear scattering events and introduces shared photon-state management to simulate complex interactions, including second-order processes such as stimulated Raman scattering (SRS). Validation against established benchmarks demonstrates excellent agreement across spatial and temporal domains. Our results reveal a nontrivial dependence of SRS efficiency on the detection and turbid media parameters, highlighting the algorithm’s capability to resolve subtle photon–photon interactions. The proposed framework offers a scalable and energy-efficient platform for advanced optical modeling, with potential applications in biomedical diagnostics, remote sensing, and photonics research. Supplementary Material File (main document - latex pdf.pdf) Download 2.85 MB Information & Authors Information Version history V1 Version 1 12 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords apple processors programming mathematical modeling monte carlo method non-linear optical effects photon transport power-effcient simulation raman scattering stimulated raman scattering Authors Affiliations Ilya Vladyko [email protected] Victoria University of Wellington School of Engineering and Computer Science View all articles by this author Vladislav Yakovlev 0000-0002-4557-1013 Texas A&M University Department of Biomedical Engineering View all articles by this author A. Doronin 0000-0003-4615-9220 Victoria University of Wellington School of Engineering and Computer Science View all articles by this author Metrics & Citations Metrics Article Usage 180 views 183 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ilya Vladyko, Vladislav Yakovlev, A. Doronin. A Power-Efficient Monte Carlo Framework for Nonlinear Light-Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering. Authorea . 12 September 2025. DOI: https://doi.org/10.22541/au.175766965.59561322/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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