---
res:
  bibo_abstract:
  - Many living and artificial systems improve their fitness or performance by adapting
    to changing environments or diverse training data. However, it remains unclear
    how environmental variation shapes adaptation, what is learned, and when memory
    of past conditions is retained. Here we show how cyclic environmental change can
    produce robust memory. Using a model athermal disordered solid trained by inverse
    design to attain target elastic properties over a prescribed range, we find that
    the system evolves toward a marginally absorbing manifold (MAM), meaning that
    training is reversible within the training range but not beyond it, which encodes
    a memory of that range. We further propose a general mechanism for MAM formation
    and memory encoding based on discontinuities in the gradient of the trained quantity.
    These results provide a simple, broadly applicable physical framework for how
    adaptive systems learn under changing environments and retain memory of past conditions.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Mengjie
      foaf_name: Zu, Mengjie
      foaf_surname: Zu
      foaf_workInfoHomepage: http://www.librecat.org/personId=26dd9e7c-e86a-11eb-a854-82ac731c9ae2
  - foaf_Person:
      foaf_givenName: Carl Peter
      foaf_name: Goodrich, Carl Peter
      foaf_surname: Goodrich
      foaf_workInfoHomepage: http://www.librecat.org/personId=EB352CD2-F68A-11E9-89C5-A432E6697425
    orcid: 0000-0002-1307-5074
  bibo_doi: 10.1103/48k2-cw3b
  bibo_issue: '2'
  bibo_volume: 4
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2835-8279
  dct_language: eng
  dct_publisher: American Physical Society@
  dct_title: 'Learning by Training: Emergent physical memory from cyclically tuning
    disordered sphere packings@'
...