---
_id: '14442'
abstract:
- lang: eng
text: In the presence of an obstacle, active particles condensate into a surface
“wetting” layer due to persistent motion. If the obstacle is asymmetric, a rectification
current arises in addition to wetting. Asymmetric geometries are therefore commonly
used to concentrate microorganisms like bacteria and sperms. However, most studies
neglect the fact that biological active matter is diverse, composed of individuals
with distinct self-propulsions. Using simulations, we study a mixture of “fast”
and “slow” active Brownian disks in two dimensions interacting with large half-disk
obstacles. With this prototypical obstacle geometry, we analyze how the stationary
collective behavior depends on the degree of self-propulsion “diversity,” defined
as proportional to the difference between the self-propulsion speeds, while keeping
the average self-propulsion speed fixed. A wetting layer rich in fast particles
arises. The rectification current is amplified by speed diversity due to a superlinear
dependence of rectification on self-propulsion speed, which arises from cooperative
effects. Thus, the total rectification current cannot be obtained from an effective
one-component active fluid with the same average self-propulsion speed, highlighting
the importance of considering diversity in active matter.
acknowledgement: MR-V and RS are supported by Fondecyt Grant No. 1220536 and Millennium
Science Initiative Program NCN19_170D of ANID, Chile. P.d.C. was supported by Scholarships
Nos. 2021/10139-2 and 2022/13872-5 and ICTP-SAIFR Grant No. 2021/14335-0, all granted
by São Paulo Research Foundation (FAPESP), Brazil.
article_number: '95'
article_processing_charge: No
article_type: original
author:
- first_name: Mauricio Nicolas
full_name: Rojas Vega, Mauricio Nicolas
id: 441e7207-f91f-11ec-b67c-9e6fe3d8fd6d
last_name: Rojas Vega
- first_name: Pablo
full_name: De Castro, Pablo
last_name: De Castro
- first_name: Rodrigo
full_name: Soto, Rodrigo
last_name: Soto
citation:
ama: Rojas Vega MN, De Castro P, Soto R. Mixtures of self-propelled particles interacting
with asymmetric obstacles. The European Physical Journal E. 2023;46(10).
doi:10.1140/epje/s10189-023-00354-y
apa: Rojas Vega, M. N., De Castro, P., & Soto, R. (2023). Mixtures of self-propelled
particles interacting with asymmetric obstacles. The European Physical Journal
E. Springer Nature. https://doi.org/10.1140/epje/s10189-023-00354-y
chicago: Rojas Vega, Mauricio Nicolas, Pablo De Castro, and Rodrigo Soto. “Mixtures
of Self-Propelled Particles Interacting with Asymmetric Obstacles.” The European
Physical Journal E. Springer Nature, 2023. https://doi.org/10.1140/epje/s10189-023-00354-y.
ieee: M. N. Rojas Vega, P. De Castro, and R. Soto, “Mixtures of self-propelled particles
interacting with asymmetric obstacles,” The European Physical Journal E,
vol. 46, no. 10. Springer Nature, 2023.
ista: Rojas Vega MN, De Castro P, Soto R. 2023. Mixtures of self-propelled particles
interacting with asymmetric obstacles. The European Physical Journal E. 46(10),
95.
mla: Rojas Vega, Mauricio Nicolas, et al. “Mixtures of Self-Propelled Particles
Interacting with Asymmetric Obstacles.” The European Physical Journal E,
vol. 46, no. 10, 95, Springer Nature, 2023, doi:10.1140/epje/s10189-023-00354-y.
short: M.N. Rojas Vega, P. De Castro, R. Soto, The European Physical Journal E 46
(2023).
date_created: 2023-10-22T22:01:13Z
date_published: 2023-10-01T00:00:00Z
date_updated: 2023-10-31T11:16:41Z
day: '01'
department:
- _id: AnSa
doi: 10.1140/epje/s10189-023-00354-y
external_id:
pmid:
- '37819444'
intvolume: ' 46'
issue: '10'
language:
- iso: eng
month: '10'
oa_version: None
pmid: 1
publication: The European Physical Journal E
publication_identifier:
eissn:
- 1292-895X
issn:
- 1292-8941
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mixtures of self-propelled particles interacting with asymmetric obstacles
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 46
year: '2023'
...
---
_id: '12791'
abstract:
- lang: eng
text: We investigate the capabilities of Physics-Informed Neural Networks (PINNs)
to reconstruct turbulent Rayleigh–Bénard flows using only temperature information.
We perform a quantitative analysis of the quality of the reconstructions at various
amounts of low-passed-filtered information and turbulent intensities. We compare
our results with those obtained via nudging, a classical equation-informed data
assimilation technique. At low Rayleigh numbers, PINNs are able to reconstruct
with high precision, comparable to the one achieved with nudging. At high Rayleigh
numbers, PINNs outperform nudging and are able to achieve satisfactory reconstruction
of the velocity fields only when data for temperature is provided with high spatial
and temporal density. When data becomes sparse, the PINNs performance worsens,
not only in a point-to-point error sense but also, and contrary to nudging, in
a statistical sense, as can be seen in the probability density functions and energy
spectra.
acknowledgement: This project has received partial funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme
(Grant Agreement No. 882340))
article_number: '16'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Patricio
full_name: Clark Di Leoni, Patricio
last_name: Clark Di Leoni
- first_name: Lokahith N
full_name: Agasthya, Lokahith N
id: cd100965-0804-11ed-9c55-f4878ff4e877
last_name: Agasthya
- first_name: Michele
full_name: Buzzicotti, Michele
last_name: Buzzicotti
- first_name: Luca
full_name: Biferale, Luca
last_name: Biferale
citation:
ama: Clark Di Leoni P, Agasthya LN, Buzzicotti M, Biferale L. Reconstructing Rayleigh–Bénard
flows out of temperature-only measurements using Physics-Informed Neural Networks.
The European Physical Journal E. 2023;46(3). doi:10.1140/epje/s10189-023-00276-9
apa: Clark Di Leoni, P., Agasthya, L. N., Buzzicotti, M., & Biferale, L. (2023).
Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using
Physics-Informed Neural Networks. The European Physical Journal E. Springer
Nature. https://doi.org/10.1140/epje/s10189-023-00276-9
chicago: Clark Di Leoni, Patricio, Lokahith N Agasthya, Michele Buzzicotti, and
Luca Biferale. “Reconstructing Rayleigh–Bénard Flows out of Temperature-Only Measurements
Using Physics-Informed Neural Networks.” The European Physical Journal E.
Springer Nature, 2023. https://doi.org/10.1140/epje/s10189-023-00276-9.
ieee: P. Clark Di Leoni, L. N. Agasthya, M. Buzzicotti, and L. Biferale, “Reconstructing
Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed
Neural Networks,” The European Physical Journal E, vol. 46, no. 3. Springer
Nature, 2023.
ista: Clark Di Leoni P, Agasthya LN, Buzzicotti M, Biferale L. 2023. Reconstructing
Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed
Neural Networks. The European Physical Journal E. 46(3), 16.
mla: Clark Di Leoni, Patricio, et al. “Reconstructing Rayleigh–Bénard Flows out
of Temperature-Only Measurements Using Physics-Informed Neural Networks.” The
European Physical Journal E, vol. 46, no. 3, 16, Springer Nature, 2023, doi:10.1140/epje/s10189-023-00276-9.
short: P. Clark Di Leoni, L.N. Agasthya, M. Buzzicotti, L. Biferale, The European
Physical Journal E 46 (2023).
date_created: 2023-04-02T22:01:11Z
date_published: 2023-03-20T00:00:00Z
date_updated: 2023-08-01T14:03:47Z
day: '20'
department:
- _id: CaMu
doi: 10.1140/epje/s10189-023-00276-9
external_id:
arxiv:
- '2301.07769'
isi:
- '000956387200001'
intvolume: ' 46'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2301.07769'
month: '03'
oa: 1
oa_version: Preprint
publication: The European Physical Journal E
publication_identifier:
eissn:
- 1292-895X
issn:
- 1292-8941
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using
Physics-Informed Neural Networks
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 46
year: '2023'
...