Evidence map›Paper›PMID 39635468›Full record

ArticleNanophotonics (Berlin, Germany)2023

Machine learning to optimize additive manufacturing for visible photonics.

Andrew Lininger, Akeshi Aththanayake, Jonathan Boyd, Omar Ali, Madhav Goel, Yangheng Jizhe, Michael Hinczewski, Giuseppe Strangi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanophotonics (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Andrew LiningerDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.ORCID https://orcid.org/0000-0001-6401-7301
Akeshi AththanayakeDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Jonathan BoydDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Omar AliDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Madhav GoelDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Yangheng JizheDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Michael HinczewskiDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.
Giuseppe StrangiDepartment of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physics of the optimization process leads to barriers for greater adoption. This issue can often result in sub-optimal optical responses in fabricated devices on both local and global scales. We propose that physics-informed design and optimization methods, and in particular physics-informed machine learning, are particularly well-suited to overcome these challenges by incorporating known physics, constraints, and fabrication knowledge directly into the design framework.

Indexed as

additive manufacturingmachine learningnanophotonicsphysics-informed machine learningtwo-photon polymerization

Identifiers

PMID39635468
PMCPMC11501914

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.