{"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/strategies-for-pretraining-neural-operators","title":"Strategies for Pretraining Neural Operators","arxiv_id":"2406.08473","date":"2024-06-12","proceeding":null,"authors":["Anthony Zhou","Cooper Lorsung","AmirPouya Hemmasian","Amir Barati Farimani"],"abstract":"Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining affects neural operators is still limited; studies generally propose tailored architectures and datasets that make it challenging to compare or examine different pretraining frameworks. To address this, we compare various pretraining methods without optimizing architecture choices to characterize pretraining dynamics on different models and datasets as well as to understand its scaling and generalization behavior. We find that pretraining is highly dependent on model and dataset choices, but in general transfer learning or physics-based pretraining strategies work best. In addition, pretraining performance can be further improved by using data augmentations. Lastly, pretraining can be additionally beneficial when fine-tuning in scarce data regimes or when generalizing to downstream data similar to the pretraining distribution. Through providing insights into pretraining neural operators for physics prediction, we hope to motivate future work in developing and evaluating pretraining methods for PDEs.","url_abs":"https://arxiv.org/abs/2406.08473v2","url_pdf":"https://arxiv.org/pdf/2406.08473v2.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":"strategies-for-pretraining-neural-operators","repo_url":"https://github.com/anthonyzhou-1/pretraining_pdes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.08473","atlas_url":"https://app.syntology.ai/?focus=2406.08473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08473"}},"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/anthonyzhou-1/pretraining_pdes","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":8,"unverified":2},"by_repo_kind":{"official":{"samples":10,"ran":8,"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":0,"samples":[{"code_sha256_prefix":"2c6f83aa983db012","entry":"Downsample","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"models/unet.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/models/unet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2c6f83aa983db012"}},{"code_sha256_prefix":"937f75fe76f87b5d","entry":"Upsample","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"models/unet.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/models/unet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"937f75fe76f87b5d"}},{"code_sha256_prefix":"892313d7e1615391","entry":"dict2tensor","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"common/utils.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"892313d7e1615391"}},{"code_sha256_prefix":"552ce5cf4cb55924","entry":"get_negative_sample","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"loss/spatiotemporal_losses.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/loss/spatiotemporal_losses.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"552ce5cf4cb55924"}},{"code_sha256_prefix":"82f3c79752f554e8","entry":"load_args","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"common/utils.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"82f3c79752f554e8"}},{"code_sha256_prefix":"cef23dd47d82c863","entry":"norm_coeffs","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"loss/statistical_losses.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/loss/statistical_losses.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cef23dd47d82c863"}},{"code_sha256_prefix":"55c5fc2140b8cefa","entry":"process_dict","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"common/utils.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/common/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"55c5fc2140b8cefa"}},{"code_sha256_prefix":"bc813d36201f1617","entry":"shuffle_odd","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"loss/spatiotemporal_losses.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/loss/spatiotemporal_losses.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bc813d36201f1617"}},{"code_sha256_prefix":"b98e86c5c3097f56","entry":"get_decoder","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"loss/spatiotemporal_losses.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/loss/spatiotemporal_losses.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b98e86c5c3097f56"}},{"code_sha256_prefix":"225f951811962879","entry":"vis_2d_plot","repo":"anthonyzhou-1/pretraining_pdes","repo_kind":"official","path":"common/plotting.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/common/plotting.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"225f951811962879"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}