@article{TEXTUAL,
      recid = {14574},
      author = {Voetberg, M. and Livaudais, Ashia and Nevin, Becky and  Paul, Omari and Nord, Brian},
      title = {DeepBench: A simulation package for physical benchmarking  data},
      journal = {Journal of Open Source Software},
      address = {2025-02-11},
      number = {TEXTUAL},
      abstract = {We introduce DeepBench, a Python library that employs  mechanistic models (i.e., analytic mathematical models) to  simulate data that represent physics-related objects and  systems: geometric shapes (e.g., polygon), physics objects  (e.g., pendulum), and astronomical objects (e.g.,  elliptical galaxy). These data take the form of images  (two-dimensional) or time series (one-dimensional). In  contrast to natural image benchmarks and complex physics  simulations, these data have simple, direct, numerical, and  traceable connections between the input data and the label  data. When seeking a quantifiable interpretation, this kind  of data is uniquely suitable for developing, calibrating,  testing, and benchmarking statistical and machine learning  models. Finally, this software package includes methods to  curate and store these datasets to maximize  reproducibility.},
      url = {http://knowledge.uchicago.edu/record/14574},
}