{"id":"https://openalex.org/W4387870206","doi":"https://doi.org/10.1109/mlsp55844.2023.10285952","title":"Physics-Informed Neural Networks for Pathloss Prediction","display_name":"Physics-Informed Neural Networks for Pathloss Prediction","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387870206","doi":"https://doi.org/10.1109/mlsp55844.2023.10285952"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026591190","display_name":"Steffen Limmer","orcid":"https://orcid.org/0000-0003-2385-7886"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Steffen Limmer","raw_affiliation_strings":["Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739"],"affiliations":[{"raw_affiliation_string":"Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056452597","display_name":"Alberto Mart\u00ednez Alba","orcid":"https://orcid.org/0000-0003-3417-2698"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alberto Martinez Alba","raw_affiliation_strings":["Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739"],"affiliations":[{"raw_affiliation_string":"Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078922078","display_name":"Nicola Michailow","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nicola Michailow","raw_affiliation_strings":["Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739"],"affiliations":[{"raw_affiliation_string":"Siemens AG, Technology, Otto-Hahn-Ring 6,Munich,Germany,81739","institution_ids":["https://openalex.org/I1325886976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026591190"],"corresponding_institution_ids":["https://openalex.org/I1325886976"],"apc_list":null,"apc_paid":null,"fwci":0.9321,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71112967,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7674946784973145},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7605122327804565},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7270895838737488},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5970615148544312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5651984214782715},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5573886632919312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.548937976360321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5195353627204895},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4962080121040344},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.43385615944862366},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20086491107940674},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11882391571998596},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08073854446411133}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7674946784973145},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7605122327804565},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7270895838737488},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5970615148544312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5651984214782715},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5573886632919312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.548937976360321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5195353627204895},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4962080121040344},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.43385615944862366},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20086491107940674},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11882391571998596},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08073854446411133},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W626427215","https://openalex.org/W1988642011","https://openalex.org/W2090527109","https://openalex.org/W2157977389","https://openalex.org/W2165798652","https://openalex.org/W2899283552","https://openalex.org/W3106552726","https://openalex.org/W3131967102","https://openalex.org/W3168386838","https://openalex.org/W3186804687"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W2080152487","https://openalex.org/W3083152911","https://openalex.org/W3022347918","https://openalex.org/W4200527723"],"abstract_inverted_index":{"This":[0,11],"paper":[1],"introduces":[2],"a":[3,43,53,87,98],"physics-informed":[4,80],"machine":[5],"learning":[6,45],"approach":[7],"for":[8,72,97],"pathloss":[9,31,103],"prediction.":[10],"is":[12,37],"achieved":[13],"by":[14],"including":[15],"in":[16,33],"the":[17,34,40,79],"training":[18,83,91],"phase":[19],"simultaneously":[20],"(i)":[21],"physical":[22],"dependencies":[23],"between":[24],"spatial":[25],"loss":[26],"field":[27],"and":[28,49,60,84],"(ii)":[29],"measured":[30],"values":[32],"field.":[35],"It":[36],"shown":[38],"that":[39],"solution":[41],"to":[42,65],"proposed":[44],"problem":[46],"improves":[47],"generalization":[48],"prediction":[50,85,104],"quality":[51],"with":[52,86],"small":[54,88],"number":[55],"of":[56,90,101],"neural":[57],"network":[58],"layers":[59],"parameters.":[61],"The":[62],"latter":[63],"leads":[64],"fast":[66],"inference":[67],"times":[68],"which":[69,93],"are":[70],"favorable":[71],"downstream":[73],"tasks":[74],"such":[75],"as":[76],"localization.":[77],"Moreover,":[78],"formulation":[81],"allows":[82],"amount":[89],"data":[92],"makes":[94],"it":[95],"appealing":[96],"wide":[99],"range":[100],"practical":[102],"scenarios.":[105]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}