{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162910"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162910","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Deep Learning for Space Object Density Distribution Prediction","abstract":"The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts.","abstract_html":"The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts.","abstract_has_math":false,"creators":["Sarangerel, Sumiyajav"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Linares, Richard"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:21:42Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/162910","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Linares, Richard"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Sarangerel, Sumiyajav"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-06T17:34:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-06T17:34:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/162910"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Deep Learning for Space Object Density Distribution Prediction"]}]}],"canonical_facts":{"dc:contributor.advisor":["Linares, Richard"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Sarangerel, Sumiyajav"],"dc:date.accessioned":["2025-10-06T17:34:14Z"],"dc:date.available":["2025-10-06T17:34:14Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["The rapid growth of artificial objects in Low Earth Orbit (LEO) has heightened concerns over orbital congestion and collision cascades, known as Kessler Syndrome. Traditional high-fidelity models, while accurate, are computationally intensive and poorly scalable. This thesis introduces a machine learning–based framework for forecasting the long-term evolution of space object density. A large dataset is generated, using the MIT Orbital Capacity Assessment Tool – Monte Carlo (MOCAT-MC), simulating thousands of scenarios across varying launch, disposal, and maneuver parameters. A Convolutional Gated Recurrent Unit (ConvGRU) is trained to predict density distributions over a 100-year horizon, achieving accurate forecasts with significantly reduced runtime. With a simple guidance mechanism, the generalization capability of the model across diverse scenarios is greatly improved. This approach offers a scalable and efficient tool for supporting future space traffic management and sustainability efforts."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/162910"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Deep Learning for Space Object Density Distribution Prediction"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:42Z"}