Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385932404> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4385932404 abstract "One of the most exciting applications of AI is automated scientific discovery based on previously amassed data, coupled with restrictions provided by the known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Of particular importance are complex dynamic systems where their time evolution is strongly influenced by varying external parameters. In this paper we develop a platform based on a generalised Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. We focus on systems whose complexity and sheer sizes render complete microscopic description impractical, and constructing theoretical macroscopic models requires extensive domain knowledge or trial-and-error. Our machine learning approach addresses this by simultaneously constructing reduced thermodynamic coordinates and interpreting the dynamics on these coordinates. We demonstrate our method by studying theoretically and validating experimentally, the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including (1) the identification of stable and transition states and (2) the control of the stretching rate. We further demonstrate the universality of our approach by applying it to an unrelated problem in a different domain: constructing macroscopic dynamics for spatial epidemics, showing that our method addresses wide scientific and technological applications." @default.
- W4385932404 created "2023-08-18" @default.
- W4385932404 creator A5017223928 @default.
- W4385932404 creator A5024183725 @default.
- W4385932404 creator A5028542802 @default.
- W4385932404 creator A5035295224 @default.
- W4385932404 creator A5069654038 @default.
- W4385932404 creator A5076965690 @default.
- W4385932404 creator A5080825351 @default.
- W4385932404 creator A5086492403 @default.
- W4385932404 date "2023-08-08" @default.
- W4385932404 modified "2023-09-27" @default.
- W4385932404 title "Constructing Custom Thermodynamics Using Deep Learning" @default.
- W4385932404 doi "https://doi.org/10.48550/arxiv.2308.04119" @default.
- W4385932404 hasPublicationYear "2023" @default.
- W4385932404 type Work @default.
- W4385932404 citedByCount "0" @default.
- W4385932404 crossrefType "posted-content" @default.
- W4385932404 hasAuthorship W4385932404A5017223928 @default.
- W4385932404 hasAuthorship W4385932404A5024183725 @default.
- W4385932404 hasAuthorship W4385932404A5028542802 @default.
- W4385932404 hasAuthorship W4385932404A5035295224 @default.
- W4385932404 hasAuthorship W4385932404A5069654038 @default.
- W4385932404 hasAuthorship W4385932404A5076965690 @default.
- W4385932404 hasAuthorship W4385932404A5080825351 @default.
- W4385932404 hasAuthorship W4385932404A5086492403 @default.
- W4385932404 hasBestOaLocation W43859324041 @default.
- W4385932404 hasConcept C116672817 @default.
- W4385932404 hasConcept C121332964 @default.
- W4385932404 hasConcept C121864883 @default.
- W4385932404 hasConcept C134306372 @default.
- W4385932404 hasConcept C154945302 @default.
- W4385932404 hasConcept C183992945 @default.
- W4385932404 hasConcept C33923547 @default.
- W4385932404 hasConcept C36503486 @default.
- W4385932404 hasConcept C41008148 @default.
- W4385932404 hasConcept C62520636 @default.
- W4385932404 hasConcept C79379906 @default.
- W4385932404 hasConcept C80444323 @default.
- W4385932404 hasConcept C99692599 @default.
- W4385932404 hasConceptScore W4385932404C116672817 @default.
- W4385932404 hasConceptScore W4385932404C121332964 @default.
- W4385932404 hasConceptScore W4385932404C121864883 @default.
- W4385932404 hasConceptScore W4385932404C134306372 @default.
- W4385932404 hasConceptScore W4385932404C154945302 @default.
- W4385932404 hasConceptScore W4385932404C183992945 @default.
- W4385932404 hasConceptScore W4385932404C33923547 @default.
- W4385932404 hasConceptScore W4385932404C36503486 @default.
- W4385932404 hasConceptScore W4385932404C41008148 @default.
- W4385932404 hasConceptScore W4385932404C62520636 @default.
- W4385932404 hasConceptScore W4385932404C79379906 @default.
- W4385932404 hasConceptScore W4385932404C80444323 @default.
- W4385932404 hasConceptScore W4385932404C99692599 @default.
- W4385932404 hasLocation W43859324041 @default.
- W4385932404 hasOpenAccess W4385932404 @default.
- W4385932404 hasPrimaryLocation W43859324041 @default.
- W4385932404 hasRelatedWork W1621539883 @default.
- W4385932404 hasRelatedWork W2010092618 @default.
- W4385932404 hasRelatedWork W2022232329 @default.
- W4385932404 hasRelatedWork W2037026168 @default.
- W4385932404 hasRelatedWork W2049798935 @default.
- W4385932404 hasRelatedWork W2088797923 @default.
- W4385932404 hasRelatedWork W2326595105 @default.
- W4385932404 hasRelatedWork W2769780345 @default.
- W4385932404 hasRelatedWork W2793902756 @default.
- W4385932404 hasRelatedWork W2922933246 @default.
- W4385932404 isParatext "false" @default.
- W4385932404 isRetracted "false" @default.
- W4385932404 workType "article" @default.