Publications

Google Scholar profile

Peer-reviewed articles

  1. M. Stern, A. Murugan, “Learning without neurons in physical systems“, Annu. Rev. Condens. Matter Phys. 14, 417-441 (2023).
  2. C. Arinze, M. Stern, S. R. Nagel, A. Murugan, “Learning to self-fold at a bifurcation“, Phys. Rev. E 107, 025001 (2023).
  3. S. Dillavou, M. Stern, A. J. Liu and D. J. Durian, “Demonstration of decentralized, physics-driven learning“, Phys. Rev. Applied 18, 014040 (2022). -Editors’ Suggestion-
  4. M. Stern, S. Dillavou, M. Z. Miskin, D. J. Durian and A. J. Liu, “Physical learning beyond the quasistatic limit“, Phys. Rev. Research 4, L022037 (2022).
  5. J. F. Wycoff, S. Dillavou, M. Stern, A. J. Liu and D. J. Durian, “Desynchronous learning in a physics-driven learning network“, J. Chem. Phys. 156, 144903 (2022).
  6. M. Stern, D. Hexner, J. W. Rocks and A. J. Liu, “Supervised learning in physical networks: From machine learning to learning machines“, Phys. Rev. X 11, 021045 (2021).
  7. M. Stern, M.B. Pinson and A. Murugan, “Continual learning of multiple memories in mechanical networks“, Phys. Rev. X 10, 031044 (2020).
  8. M. Stern, C. Arinze, L. Perez, S. E. Palmer and A. Murugan, “Supervised learning through physical changes in a mechanical system“, Proc. Natl. Acad. Sci. USA 117, 26 (2020).
  9. M. Stern, V. Jayaram and A. Murugan, “Shaping the topology of folding pathways in mechanical systems“, Nat. Commun. 9, (1) 4303 (2018).
  10. M. Stern, M.B. Pinson and A. Murugan, “The complexity of folding self-folding origami“, Phys. Rev. X 7, 041070 (2017).
  11. M.B. Pinson*, M. Stern*, A.C. Ferrero, T. A. Witten, E. Chen and A. Murugan, “Self-folding origami at any energy scale“,  Nat. Commun. 8, 15477 (2017).
  12. M. H. K. Schaarsberg, I. R. Peters, M. Stern, K. Dodge, W. W. Zhang and H. M. Jaeger, “From splashing to bouncing: The influence of viscosity on the impact of suspension droplets on a solid surface“, Phys. Rev. E 93, 062609 (2016).

Conference papers

  1. S. Dillavou, B. Beyer, M. Stern, M. Z. Miskin, A. J. Liu and D. J. Durian, “Circuits that train themselves: decentralized, physics-driven learning“, AI and Optical Data Sciences IV (SPIE 2023), San Francisco, California, United States.
  2. M. Stern, S. Dillavou, M. Z. Miskin, D. J. Durian and A. J. Liu, “Out of equilibrium learning dynamics in physical allosteric resistor networks“, Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), Vancouver, Canada.
  3. M. Stern, C. de Mulatier, P. Fleig and V. Balasubramanian, “Curious exploration in complex environments based on Hopfield networks“, Towards Curious Robots: Modern Approaches for Intrinsically Motivated Intelligent Behavior (ICRA 2021), Xi’an, China.
  4. M. Stern, D. Hexner, J. W. Rocks and A. J. Liu, “Flow networks as learning machines“, Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada.

Preprints

  1. M. Stern, A. J. Liu and V. Balasubramanian, “The physical effects of learning“, arXiv:2306.12928.

Patents

  1. S. Dillavou, D. J. Durian, A. J. Liu, M. Stern, M. Z. Miskin, “Coupled networks for physics-based machine learning“, US20220383205A1 (Dec. 2022).

Recorded talks

 

From machine learning to learning machines – Science on the rocks (IAC and ScienceAbroad), July 18th, 2020

 

Ph.D. defense – November 4th, 2019

 


Media coverage

How to Make the Universe Think for Us – Quanta Magazine (May 2022)

An illustration of a spherical universe sitting inside a machine hooked up to wires.

 

Simple electrical circuit learns on its own-with no help from a computer – Science (March 2022)

A panel of resistors

 

Programming Matter to do a Computer’s Job – APS News (May 2021)

Researchers at the University of Pennsylvania have developed an electrical circuit that learned to identify different kinds of iris flowers.

 

Why Self-folding Objects Get Stuck – Futurity (January 2018)