{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iterative-sizing-field-prediction-for","title":"Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations","arxiv_id":"2406.14161","date":"2024-06-20","proceeding":null,"authors":["Niklas Freymuth","Philipp Dahlinger","Tobias Würth","Philipp Becker","Aleksandar Taranovic","Onno Grönheim","Luise Kärger","Gerhard Neumann"],"abstract":"Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approximations such as the Finite Element Method (FEM). The cost and accuracy of the FEM scale with the resolution of the underlying computational mesh. To balance computational speed and accuracy meshes with adaptive resolution are used, allocating more resources to critical parts of the geometry. Currently, practitioners often resort to hand-crafted meshes, which require extensive expert knowledge and are thus costly to obtain. Our approach, Adaptive Meshing By Expert Reconstruction (AMBER), views mesh generation as an imitation learning problem. AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh on a given intermediate mesh, creating a more accurate subsequent mesh. This iterative process ensures efficient and accurate imitation of expert mesh resolutions on arbitrary new geometries during inference. We experimentally validate AMBER on heuristic 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline.","url_abs":"https://arxiv.org/abs/2406.14161v1","url_pdf":"https://arxiv.org/pdf/2406.14161v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iterative-sizing-field-prediction-for","repo_url":"https://github.com/NiklasFreymuth/AMBER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"}],"methods":[{"method_slug":"fem","method_name":"FEM"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.14161","atlas_url":"https://app.syntology.ai/?focus=2406.14161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14161"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/NiklasFreymuth/AMBER","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"d5bee880aedf8059","entry":"get_ckpts","repo":"niklasfreymuth/amber","repo_kind":"official","path":"evaluation.py","file_url":"https://github.com/niklasfreymuth/amber/blob/HEAD/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d5bee880aedf8059"}},{"code_sha256_prefix":"e9b083e5a6fbfa7a","entry":"get_evaluation_step_name","repo":"niklasfreymuth/amber","repo_kind":"official","path":"src/algorithm/core/amber.py","file_url":"https://github.com/niklasfreymuth/amber/blob/HEAD/src/algorithm/core/amber.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e9b083e5a6fbfa7a"}},{"code_sha256_prefix":"f4862a3cc27e0c20","entry":"get_scatter_reduce","repo":"NiklasFreymuth/AMBER","repo_kind":"official","path":"src/algorithm/util/amber_util.py","file_url":"https://github.com/NiklasFreymuth/AMBER/blob/HEAD/src/algorithm/util/amber_util.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f4862a3cc27e0c20"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}