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1. Spontaneous reheating of crystallizing lava

   https://doi.org/10.1130/G49148.1  

   Whittington, Alan G.; Sehlke, Alexander (August 2021, Geology)

   Abstract We show that recalescence, or spontaneous reheating of a cooling material due to rapid release of latent heat, can occur during disequilibrium crystallization of depolymerized Mg-rich melts. This can only happen at fast cooling rates, where the melt becomes undercooled by tens to hundreds of degrees before crystallization begins. Using a forward-looking infrared (FLIR) camera, we documented recalescence in pyroxene (Fe, Mg)SiO3 and komatiite lavas that initially cooled at 25–50 °C s–1. Local heating at the crystallization front exceeds 150 °C for the pyroxene and 10 °C for komatiite and lasts for several seconds as the crystallization front migrates through. We determined the latent heat release by differential scanning calorimetry to be 440 J g–1 for pyroxene and 275 J g–1 for komatiite with a brief power output of ∼100 W g–1 or ∼300 MW m–3. Recalescence may be a widespread process in the solar system, particularly in lava fountains, and cooling histories of mafic pyroclasts should not be assumed a priori to be monotonic. 

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2. When it rains, lava pours

   Manga, M (January 2020, 07 Nature)
3. Standing waves in high speed lava channels: A tool for constraining lava dynamics and eruptive parameters

   https://doi.org/10.1016/j.jvolgeores.2020.106944  

   Le Moigne, Y.; Zurek, J.M.; Williams-Jones, G.; Lev, E.; Calahorrano-Di Patre, A.; Anzieta, J. (September 2020, Journal of Volcanology and Geothermal Research)

   null (Ed.)
4. Analog experiments of lava flow emplacement

   https://doi.org/10.4401/ag-7843  

   Lev, Einat; Rumpf, Elise (January 2018, Annals of Geophysics)
5. Parallelized Multi-Agent Bayesian Optimization in Lava

   https://doi.org/10.1109/ICONS62911.2024.00051  

   Snyder, Shay; Gobin, Derek; Clerico, Victoria; Risbud, Sumedh R; Parsa, Maryam (July 2024, IEEE)

   In parallel with the continuously increasing parameter space dimensionality, search and optimization algorithms should support distributed parameter evaluations to reduce cumulative runtime. Intel’s neuromorphic optimization library, Lava-Optimization, was introduced as an abstract optimization system compatible with neuromorphic systems developed in the broader Lava software framework. In this work, we introduce Lava Multi-Agent Optimization (LMAO) with native support for distributed parameter evaluations communicating with a central Bayesian optimization system. LMAO provides an abstract framework for deploying distributed optimization and search algorithms within the Lava software framework. Moreover, LMAO introduces support for random and grid search along with process connections across multiple levels of mathematical precision. We evaluate the algorithmic performance of LMAO with a traditional non-convex optimization problem, a fixed-precision transductive spiking graph neural network for citation graph classification, and a neuromorphic satellite scheduling problem. Our results highlight LMAO’s efficient scaling to multiple processes, reducing cumulative runtime and minimizing the likelihood of converging to local optima. 

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