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High-Performance Computing in the Time of Uncertainty
Published on Dec 31, 20152149 Views
More than ever computers hold the promise of enhancing our cognitive capacity to solve complex problems. Our generation has access to extraordinary computing technologies and unprecedented information
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Chapter list
Visokozmogljivi računalniki v času negotovosti00:00
With03:26
CFD and Technology03:45
CFD and Medicine04:12
Cores, particles, efficiency05:05
Tumor Induced Angiogenesis - 106:06
Angiogenesis: a multi-scale process06:57
Tumor Induced Angiogenesis - 207:34
CFD: Then and Now08:22
(Adaptive) Algorithms + (Super) Computers09:24
The imperfect paths to knowledge09:52
HPC Challenges11:11
Nano, macro11:43
The gap12:26
Moore's law12:35
Bubbles and Cavitation13:56
Bubble collapse15:01
Cavitation and destruction15:11
State of the art15:53
Govering equations16:51
What is the best we can get ?17:41
Reconstructions and Fluxes18:14
The finite volume method18:49
Roofline Model19:12
Roofline and the 7 Dwarfs22:19
Core/Node Performance: The Roofline of BG/Q23:24
Performance/comparisons24:13
Cloud Cavitation Collapse - 125:41
Cloud Cavitation Collapse - 225:53
Cloud Cavitation Collapse - 326:23
Cloud Cavitation Collapse - 427:07
Cloud Cavitation Collapse - 527:26
Shock-Bubble Interactions27:33
With experiments28:27
In Pharmaceutical R&D28:33
Prognosis29:22
μ-Fluidics for CTCs31:00
Microfluids31:57
Dissipative particle dynamics34:07
N-body Interactions + Stochastics34:27
Red Blood Cells34:44
Solvent, cells, μ-Fluidic device35:05
In-vitro35:37
1 to 1 with μFluidic Devices at subcell resolution37:13
The data37:39
Solving problems38:06
Gravity38:57
Components and Challenges40:15
The imperfect paths to knowledge40:24
Molecular Dynamics40:49
Sources of Uncertainty in Water-Graphite Systems41:24
Wetting of Graphene by Water Droplets42:04
Wetting AND MD potentials42:25
Calibrate water-graphite potentials from experiments42:55
Water Flow in Carbon Nanotubes43:45
Water Transport in CNTS43:59
Enhanced flow in carbon nanotubes44:45
Fast Mass Transport 45:16
MD in periodic domains45:44
MD of water through simplified CNT membrane46:26
Fast Mass Transport Through Sub-2 Nanometer - 146:52
Large Scale MD Simulations of Water Transport47:40
Fast Mass Transport Through Sub-2 Nanometer - 250:01
Fast Mass Transport Through Sub-2 Nanometer - 350:15
Sir Arthur Eddington50:22
Scientific Reasoning – The Bayesian Approach - 150:33
Scientific Reasoning – The Bayesian Approach - 250:48
Uncertainties in MD Simulations51:10
Water Contact Angle51:32
Bayesian UQ: Calibration and Model Selection51:52
Bayesian Inference for Uncertainty Quantfication52:41
Bayesian Uncertainty Propagation53:18
Well depth and cut-off53:41
Water Contact Angle - 154:17
Water Contact Angle - 255:21
Water Transport in Carbon Nanotubes55:32
How fast does water flow in Carbon Nanotubes55:54
Data can be Heterogeneous56:27
MD predictions are Heterogeneous57:04
Describing the Stochastic Model Classes57:20
Hierarchical Bayesian Framework58:33
Inconsistent Posteriors - Argon59:06
Effect of stochastic model on predictions59:39
Calibrating MD simulation for water01:00:36
Bayesian Inference using Individual Data Set01:00:52
Posterior of Model Parameters01:01:30
Summary/Outlook01:02:06
Summary01:02:09
Thank You01:03:53