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Found 7 papers, 2 papers with code
On random number generators and practical market efficiency
Limited prior studies in the operational research literature have investigated tests designed for random number generators to check for these informational efficiencies.
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge.
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties.
Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems.
Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computation Methodology 85A40, 68W10, 62G07, 62P35
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems.
Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks
Astronomy of the 21st century increasingly finds itself with extreme quantities of data.
