Special Issue: Physics-Informed Machine Learning for Advanced Manufacturing
Bibliographic record
Abstract
Yuebin GuoYuebin Guo This Special Issue serves as a bridge between the ASME Journal of Manufacturing Science and Engineering (JMSE) and the global community of artificial intelligence manufacturing researchers. The primary objective of the Special Issue is to collect high-level scientific articles in the emerging area of physics-informed machine learning (PIML) for advanced manufacturing and push the boundaries of knowledge. Contributions are sought in recent advances, challenges, and future directions of PIML model development at the levels of processes, machines, and systems.A team of Guest Editors has been setup to collect as diverse selection articles as possible. The team is led by Professor Yuebin Guo (Rutgers University-New Brunswick, Piscataway, NJ) and consists of Professor Yusuf Altintas (The University of British Columbia, Vancouver, BC, Canada), Professor Qing Chang (University of Virginia, Charlottesville, VA), Professor Robert Gao (Case Western Reserve University, Cleveland, OH), Professor Weihong Grace Guo (Rutgers University-New Brunswick, Piscataway, NJ), Dr. Andy Henderson (Hendtech LLC, Greenville, SC), Dr. Jaydeep Karandikar (Oak Ridge National Laboratory, Oak Ridge, TN), and Professor Tony Schmitz (University of Tennessee, Knoxville, TN).While this collection of articles represents only an initiative to define and shape the nascent area of PIML for advanced manufacturing, it demonstrates a snapshot of the current landscape of the vibrant area. These articles cover a wide array of PIML topics, including forward and inverse predictions, uncertainty quantification and Bayesian optimization, data assimilation, process optimization, machine dynamics, quality control, and reduced-order models. The collection of articles is from Oct. 2023 to Jan. 2024. Each article underwent a rigorous peer review process, which is a hallmark of JMSE.The Guest Editors would like to acknowledge JMSE Editor-in-Chief Professor Albert Shih for providing timely consultation and feedback during the process. We also want to acknowledge Jennifer Smith and Emily Bosco, who provided continuous and timely editorial assistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".