396 research outputs found
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
We explore the potential of feed-forward deep neural networks (DNNs) for
emulating cloud superparameterization in realistic geography, using offline
fits to data from the Super Parameterized Community Atmospheric Model. To
identify the network architecture of greatest skill, we formally optimize
hyperparameters using ~250 trials. Our DNN explains over 70 percent of the
temporal variance at the 15-minute sampling scale throughout the mid-to-upper
troposphere. Autocorrelation timescale analysis compared against DNN skill
suggests the less good fit in the tropical, marine boundary layer is driven by
neural network difficulty emulating fast, stochastic signals in convection.
However, spectral analysis in the temporal domain indicates skillful emulation
of signals on diurnal to synoptic scales. A close look at the diurnal cycle
reveals correct emulation of land-sea contrasts and vertical structure in the
heating and moistening fields, but some distortion of precipitation.
Sensitivity tests targeting precipitation skill reveal complementary effects of
adding positive constraints vs. hyperparameter tuning, motivating the use of
both in the future. A first attempt to force an offline land model with DNN
emulated atmospheric fields produces reassuring results further supporting
neural network emulation viability in real-geography settings. Overall, the fit
skill is competitive with recent attempts by sophisticated Residual and
Convolutional Neural Network architectures trained on added information,
including memory of past states. Our results confirm the parameterizability of
superparameterized convection with continents through machine learning and we
highlight advantages of casting this problem locally in space and time for
accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of
Advances in Modeling Earth Systems April 202
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Optimal gait and form for animal locomotion
We present a fully automatic method for generating gaits and morphologies for legged animal locomotion. Given a specific animal’s shape we can determine an efficient gait with which it can move. Similarly, we can also adapt the animal’s morphology to be optimal for a specific locomotion task. We show that determining such gaits is possible without the need to specify a good initial motion, and without manually restricting the allowed gaits of each animal. Our approach is based on a hybrid optimization method which combines an efficient derivative-aware spacetime constraints optimization with a derivative-free approach able to find non-local solutions in high-dimensional discontinuous spaces. We demonstrate the effectiveness of this approach by synthesizing dynamic locomotions of bipeds, a quadruped, and an imaginary five-legged creature
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