{"id":"W4415397854","doi":"10.1016/j.neunet.2026.109236","title":"Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Feature (linguistics); Embedding; Artificial neural network; Convergence (economics); Feature learning; Limiting; Consistency (knowledge bases); Key (lock)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002910914,0.00149391,0.001345583,0.0004960931,0.0006677923,0.0009320101,0.002879188,0.002398943,0.003143576],"category_scores_gemma":[0.01647099,0.001053283,0.0007231433,0.0006607012,0.001191861,0.003005283,0.004444167,0.003036742,0.0006784652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007871606,"about_ca_system_score_gemma":0.00191621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003981706,"about_ca_topic_score_gemma":0.006755403,"domain_scores_codex":[0.9991364,0.0003399194,0.00003395003,0.0002143478,0.0002124177,0.00006309868],"domain_scores_gemma":[0.9971584,0.00161345,0.0002290532,0.0005786938,0.0003019718,0.0001185011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000741861,0.000143041,0.001671025,0.000304413,0.0002361288,0.0001801614,0.000211019,0.6496799,0.01067183,0.03992041,0.007028372,0.2892118],"study_design_scores_gemma":[0.00002048379,0.00004133398,0.0001692091,0.00001126398,0.0000188542,0.00002300128,0.000006667487,0.9752832,0.002651773,0.02100287,0.0007623738,0.000008946537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01534204,0.0004412268,0.9817125,0.0004189456,0.00009637182,0.00004276257,0.0001286917,0.001041216,0.0007761119],"genre_scores_gemma":[0.5209241,0.0007663139,0.4685294,0.0009191668,0.0002858478,0.000272081,0.000780419,0.0007223838,0.006800249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003981706,"threshold_uncertainty_score":0.01539457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02511426141977303,"score_gpt":0.2824379685600887,"score_spread":0.2573237071403157,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}