{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:27:55Z","timestamp":1785432475429,"version":"3.56.0"},"reference-count":37,"publisher":"Optica Publishing Group","issue":"7","license":[{"start":{"date-parts":[[2027,6,29]],"date-time":"2027-06-29T00:00:00Z","timestamp":1814227200000},"content-version":"am","delay-in-days":365,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#AM"},{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/00rk2pe57","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Naval Research Laboratory Optical\n\t\t\tSciences Division.","id":[{"id":"https:\/\/ror.org\/03j9xzm13","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Soc. Am. A"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>This experimental study investigates whether the temporal evolution of\n\t\t\t\treceived laser-beam intensity can be forecast from intensity images alone\n\t\t\t\tunder controlled underwater turbulence. Conventional adaptive-optics\n\t\t\t\tsystems commonly rely on present wavefront measurements and are therefore\n\t\t\t\treactive to measured turbulence-induced distortions. We examine a\n\t\t\t\tdifferent prerequisite problem: whether established deep-learning\n\t\t\t\tcomponents can be assembled into short-horizon predictors of\n\t\t\t\texperimentally measured intensity evolution, without phase measurements or\n\t\t\t\titerative wavefront reconstruction. Synchronized pupil-plane and\n\t\t\t\tfocal-plane image sequences are recorded after propagation through a\n\t\t\t\tRayleigh\u2013B\u00e9nard water-tank turbulence emulator at three turbulence\n\t\t\t\tstrengths. Two plane-matched forecasting algorithms are evaluated. A\n\t\t\t\tlatent-space autoencoder\u2013ConvLSTM is applied to the compact, wandering\n\t\t\t\tfocal-plane intensity pattern, while a 3D spatiotemporal attention network\n\t\t\t\tpredicts inter-frame intensity changes in the extended pupil-plane\n\t\t\t\tdistribution. Forecasts are evaluated at horizons of\n\t\t\t\t+1,\n\t\t\t\t+2,\n\t\t\t\tand\n\t\t\t\t+3\n\t\t\t\tframes using mean-squared error and structural similarity index, with\n\t\t\t\tpersistence baselines, regime-specific and all-regime training\n\t\t\t\tcomparisons, metric-domain analysis, OAM-mode evaluations, and a\n\t\t\t\tcross-algorithm diagnostic. The results show that the learned models\n\t\t\t\tproduce nontrivial short-horizon forecasts beyond persistence, that the\n\t\t\t\ttwo measurement planes favor different algorithmic designs, and that\n\t\t\t\tsimilar intensity-forecasting behavior extends across Gaussian and beam\n\t\t\t\tcarrying orbital angular momentum (OAM) morphologies while remaining\n\t\t\t\tlimited to intensity prediction rather than phase structure, modal purity,\n\t\t\t\tor topological-charge recovery.<\/jats:p>","DOI":"10.1364\/josaa.601066","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T13:00:08Z","timestamp":1781182808000},"page":"C77","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning forecasts of laser intensity evolution through underwater turbulence"],"prefix":"10.1364","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6624-7514","authenticated-orcid":true,"given":"S.","family":"Avramov-Zamurovic","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znex860","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Fleming","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znex860","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Kincade","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znex860","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Taylor","sequence":"additional","affiliation":[{"name":"United States Naval Academy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6026-8184","authenticated-orcid":true,"given":"O.","family":"O\u2019Malley","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00znex860","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5519-4349","authenticated-orcid":true,"given":"K. 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