James Crutchfield

About

James Crutchfield received his B.A. summa cum laude in Physics and Mathematics from the University of California, Santa Cruz, in 1979 and his Ph.D. in Physics there in 1983. He is currently a Distinguished Professor of Physics at the University of California, Davis, where he directs the Complexity Sciences Center. Prior to joining UC Davis, he was a Research Professor at the Santa Fe Institute, where he led the Dynamics of Learning Group, and an Adjunct Professor of Physics at the University of New Mexico, Albuquerque. Before his tenure at the Santa Fe Institute beginning in 1997, he was a Research Physicist in the Physics Department at the University of California, Berkeley, since 1985. He has also held visiting scholar positions at several prestigious institutions including the Institute for Advanced Study at the University of Amsterdam, the Sloan Center for Theoretical Neurobiology at UCSF, the Physics Department at Caltech, and the Redwood Center for Theoretical Neuroscience. His postdoctoral fellowships include the Miller Institute for Basic Research in Science at UCB and the IBM Postdoctoral Fellowship in Condensed Matter Physics at UCB, among others. Over the last four decades, Professor Crutchfield has worked extensively in nonlinear dynamics, solid-state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions, chaos, and pattern formation. His current research interests focus on computational mechanics, the physics of complexity, statistical…

Research topics

• Computer Science
• Machine Learning
• Data Mining
• Geology
• Seismology
• Artificial Intelligence
• Thermodynamics
• Algorithm
• Statistical physics
• Physics

Selected publications

• Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

  Earth and Space Science · 2021 · 47 citations

  Abstract We propose a new machine learning‐based method for nowcasting earthquakes to image the time‐dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross‐correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via u…

  DOI

• Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine Learning: A Comparison of Two Methods

  Surveys in Geophysics · 2021 · 37 citations

  Senior authorCorresponding
  DOI

• Nonequilibrium thermodynamics of erasure with superconducting flux logic

  Physical Review Research · 2020 · 29 citations

  We implement a thermal-fluctuation-driven logical bit reset on a superconducting flux logic cell. We show that the logical state of the system can be continuously monitored with only a small perturbation to the thermally activated dynamics at 500 mK. We use the trajectory information to derive a single-shot estimate of the work performed on the system per logical cycle. We acquire a sample of 105 erasure trajectories per protocol and show that the work histograms agree with both microscopic theo…

  DOI

• Branching States as The Emergent Structure of a Quantum Universe

  Quantum · 2024-10-10 · 9 citations

  articleOpen accessSenior author

  Quantum Darwinism builds on decoherence theory to explain the emergence of classical behavior in a fundamentally quantum universe. Within this framework we prove two crucial insights about the emergence of classical phenomenology, centered around quantum discord as the measure of quantumness of correlations. First, we show that the so-called branching structure of the joint state of the system and environment is the only one compatible with zero discord. Second, we prove that for small but nonze…

  PublisherOA PDFDOI

• Learning entropy production from underdamped Langevin trajectories

  Physical review. E · 2024-12-27 · 6 citations

  articleSenior author

  Entropy production (EP) is a central quantity in nonequilibrium physics as it monitors energy dissipation, irreversibility, and free energy differences during thermodynamic transformations. Estimating EP, however, is challenging both theoretically and experimentally due to limited access to the system dynamics. For overdamped Langevin dynamics and Markov jump processes it was recently proposed that, from thermodynamic uncertainty relations (TURs), short-time cumulant currents can be used to esti…

  PublisherDOI

Frequent coauthors

• Andrea Donnellan

  Jet Propulsion Laboratory

  50 shared

• Ryan G. James

  47 shared

• John B. Rundle

  46 shared

• Sarah Marzen

  42 shared

• Christopher J. Ellison

  University of Minnesota

  41 shared

• John R. Mahoney

  University of California, Davis

  38 shared

• Melanie Mitchell

  36 shared

• Geoffrey Fox

  35 shared

Awards & honors

• Member, National Academy Committee on Information Technology…

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