@article{14442,
  abstract     = {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.},
  author       = {Rojas Vega, Mauricio Nicolas and De Castro, Pablo and Soto, Rodrigo},
  issn         = {1292-895X},
  journal      = {The European Physical Journal E},
  number       = {10},
  publisher    = {Springer Nature},
  title        = {{Mixtures of self-propelled particles interacting with asymmetric obstacles}},
  doi          = {10.1140/epje/s10189-023-00354-y},
  volume       = {46},
  year         = {2023},
}

@article{12791,
  abstract     = {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.},
  author       = {Clark Di Leoni, Patricio and Agasthya, Lokahith N and Buzzicotti, Michele and Biferale, Luca},
  issn         = {1292-895X},
  journal      = {The European Physical Journal E},
  number       = {3},
  publisher    = {Springer Nature},
  title        = {{Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed Neural Networks}},
  doi          = {10.1140/epje/s10189-023-00276-9},
  volume       = {46},
  year         = {2023},
}