{"id":162,"date":"2023-02-27T18:46:39","date_gmt":"2023-02-27T18:46:39","guid":{"rendered":"https:\/\/atmos.ucla.edu\/space\/?page_id=162"},"modified":"2026-09-10T20:29:22","modified_gmt":"2026-09-10T20:29:22","slug":"areas-of-research","status":"publish","type":"page","link":"https:\/\/atmos.ucla.edu\/space\/areas-of-research\/","title":{"rendered":"Areas of Research"},"content":{"rendered":"\n
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Research \u00b7 UCLA Space Physics & AI Lab<\/p>\n

From plasma waves to intelligent prediction.<\/h2>\n
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We investigate how plasma waves are born, how they travel, and how they exchange energy with charged particles. Our group connects first-principles physics with spacecraft observations, controlled experiments, numerical simulations, and machine intelligence to understand\u2014and ultimately predict\u2014the dynamic space environment.<\/p><\/div>\n

WAVE\u2013PARTICLE INTERACTIONS
PLASMA WAVES
LABORATORY EXPERIMENTS
NUMERICAL SIMULATION
MACHINE LEARNING & AI<\/p><\/div>\n<\/div>\n<\/div>\n\n

\"Conceptual<\/figure>\n\n\n
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01 \u00b7 Fundamental physics<\/p>

Particles, waves, and the energy between them.<\/h2><\/div>\n
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Wave\u2013particle interactions<\/h3>\n

Energetic particles and plasma waves continually exchange energy. We study the resonant and nonlinear processes that accelerate radiation-belt electrons, scatter particles into the atmosphere, and shape auroral emissions. The goal is to connect microscopic interactions with system-scale behavior.<\/p>\n

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Plasma-wave excitation, propagation, and distribution<\/h3>\n

We ask where waves are generated, how they refract and propagate through an inhomogeneous magnetosphere, and why they become intense in some regions and quiet in others. Our work spans chorus, plasmaspheric hiss, electromagnetic ion cyclotron waves, magnetosonic waves, and related emissions that regulate energetic particles from Earth to Jupiter.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n

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02 \u00b7 A connected toolkit<\/p>\n

The same question, tested four ways.<\/h2>\n
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OBSERVE<\/p>

Spacecraft data<\/h3>

Multi-mission measurements reveal particles and waves across locations, energies, and timescales.<\/p><\/div>\n

ISOLATE<\/p>

Laboratory plasma experiments<\/h3>

Controlled experiments reproduce key features of wave generation and particle scattering so individual mechanisms can be tested directly.<\/p><\/div>\n

SIMULATE<\/p>

Numerical models<\/h3>

Theory, particle tracing, diffusion models, and large-scale simulations connect local interactions to evolving magnetospheric systems.<\/p><\/div>\n

LEARN<\/p>

Machine intelligence<\/h3>

Data-driven models reconstruct missing structure, identify physical drivers, and make complex environments more predictable.<\/p><\/div>\n<\/div>\n<\/div>\n\n

\"Conceptual<\/figure>\n\n\n
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03 \u00b7 Machine learning & AI<\/p>

AI as a scientific instrument.<\/h2><\/div>\n

We use machine learning to do more than make accurate forecasts. Our models reconstruct global, time-dependent plasma environments from sparse measurements gathered by moving spacecraft; specify energetic-particle conditions across energy, location, and time; and help distinguish the physical drivers of enhancement, loss, and quiet intervals.<\/p>\n

Projects such as the ORIENT radiation-belt model demonstrate the path from fundamental research to usable space-weather capability. At the same time, interpretable and physics-informed AI turns model behavior back into testable scientific questions\u2014an approach we are extending across space science and the physical sciences more broadly.<\/p>\n

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RECONSTRUCT \u00b7 INTERPRET \u00b7 PREDICT \u00b7 DISCOVER<\/p><\/div>\n<\/div>\n<\/div>\n\n\n

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One program \u00b7 many scales<\/p>

Discovery moves in both directions.<\/h2>

Measurements motivate theory. Experiments isolate mechanisms. Simulations connect scales. AI reveals patterns and raises new physical questions. Together, these methods let us move from the smallest resonant interaction to the behavior of an entire magnetosphere\u2014and from explanation toward prediction.<\/p><\/div>\n

Next layer<\/p>

Projects & publications<\/h3>

The next iteration will connect each research theme to current projects, representative papers, datasets, software, and the people leading the work.<\/p><\/div><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Research \u00b7 UCLA Space Physics & AI Lab From plasma waves to intelligent prediction. We investigate how plasma waves are<\/p>\n","protected":false},"author":54,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"front-page-no-banner.php","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-162","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/pages\/162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/users\/54"}],"replies":[{"embeddable":true,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/comments?post=162"}],"version-history":[{"count":41,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/pages\/162\/revisions"}],"predecessor-version":[{"id":569,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/pages\/162\/revisions\/569"}],"wp:attachment":[{"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/media?parent=162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/categories?post=162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/atmos.ucla.edu\/space\/wp-json\/wp\/v2\/tags?post=162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}