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I think there's too much emphasis on BTC/ETH/whatever ETFs, and not enough emphasis on making it easier for people to buy $5 to $100 in cryptocurrency via cards at corner stores. The former is better for pumping price, but the latter is much better for actual adoption.

Big whirls have little whirls, That feed on their velocity; And little whirls have lesser whirls, And so on to viscosity. - Lewis Fry Richardson instagram.com/p/CDpAwN0FCwy/…

Thank you Skoltech_ru SMILES - Summer School of Machine Learning at SK for organizing such a nice summer school on Machine Learning and accepting me :). Very interesting lectures.

#RAISE2020 is all set to take place from Oct 5 to 9, 2020 in a virtual avatar. The summit will host some of the world's most renowned AI leaders and decision makers - to understand how AI can be mobilised for greater social good. Register NOW: bit.ly/3cdUBA5 #AIforAll

Congratulations prof PS Koutsourelakis and Sebastian.

Interested in using unlabeled data (i.e. just inputs) and physical constraints in constructing efficient surrogates for PDEs with high-dimensional parametric inputs in the Small-Data regime? More information in the newly-published J. of Comp. Phys. paper authors.elsevier.com/a/1cel5508Hokgj

Interested about efficient surrogate for turbulent thermal convection? Or ML for fluid mechanics in general 👇 Work from my time in LISN ( Université Paris-Saclay) #ML #turbulence #PINNs

Check out our new #JCP paper about: Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection The paper is freely available for next 50 days, click below. lnkd.in/eArPTE-c #turbulence #AI #PINN #fluid

Interested in learning a probabilistic, #data-driven #turbulence closure model from sparse velocity/pressure observations, while quantifying the model form error? (Joint work with prof. PS Koutsourelakis ) #turbulence #RANS #datadriven #probabilisticmachinelearning #UQ

Model uncertainty remains one the most difficult topics in #Uncertainty_Quantification. Here is our attempt to quantify it in the context of the Reynolds-Averaged Navier-Stokes model #RANS, in a physically meaningful manner that still allows probabilistic predictive estimates.

LVM3 M4/Chandrayaan-3 Mission: LVM3 M4 vehicle🚀 successfully launched Chandrayaan-3🛰️ into orbit.

New work with Atul Agrawal on a probabilistic, data-driven closure model for RANS equations sciencedirect.com/science/articl…

New paper on probabilistic data driven model for turbulence closure with PS Koutsourelakis in Elsevier Physics Journal of Computational Physics. Open Source link: sciencedirect.com/science/articl… TU München #RANS #turbulence #DataDriven #SciML

Have an optimization problem involving a real-world physics-based simulator? The simulator is stochastic, 💵💵, black-box, multi-modal, and high dimensional? We might have a solution. Check out: hal.science/hal-04659802 (w Kislaya Ravi, PS Koutsourelakis , J.H. Bungartz)

ARTICLE: From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties ✍️Atul Agrawal, PS Koutsourelakis (TU München), Erik Tamsen and J.F. Unger (BAM_DE) 👉bit.ly/4eIo6Jo

New paper alert. "Stochastic black-box optimization using multifidelity score function estimator," published in Machine Learning: Science and Technology (IOP Publishing ) w\ kislaya ravi PS Koutsourelakis prof. Hans Bungartz. iopscience.iop.org/article/10.108… #BlackBoxOptimization #SciML #UQ #ML