Pith. sign in

REVIEW 1 cited by

Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2102.07109 v1 pith:L66HX5FV submitted 2021-02-14 cs.LG cs.SYeess.SY

classification cs.LGcs.SYeess.SY
keywords learningmachinemethodsresearchapplicationscurrentdesignengines
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The last years have witnessed an enormous interest in the use of artificial intelligence methods, especially machine learning algorithms. This also has a major impact on aerospace engineering in general, and the design and operation of liquid rocket engines in particular, and research in this area is growing rapidly. The paper describes current machine learning applications at the DLR Institute of Space Propulsion. Not only applications in the field of modeling are presented, but also convincing results that prove the capabilities of machine learning methods for control and condition monitoring are described in detail. Furthermore, the advantages and disadvantages of the presented methods as well as current and future research directions are discussed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule

    physics.flu-dyn 2026-03 conditional novelty 5.0 of 10

    A Fourier-enhanced neural field predicts steady 3D hypersonic flow around the Orion capsule in seconds, matching CFD snapshots at held-out angles of attack better than graph networks.

Pith tools