COVID-19 Estimator

HOME
Research
TEACHING
Software
PEOPLE
Talks
PUBLICATIONS
Gallery
CONTACT

mad20_Notebook3.1_NewtonSecantMethods.ipynb

Home /
mad20_Notebook3.1_NewtonSecantMethods.ipynb

mad20_Notebook3.1_NewtonSecantMethods.ipynb

Post by: cselab
1:58PM Mar 09, 2020
Comments off

mad20_Notebook3.1_NewtonSecantMethods.ipynb

Categories:

Post navigation

Search

Search for:

Recent Posts

Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics

Our paper “Robust optimal sensor configuration using the value of information” was published in Structural Control and Health Monitoring

Our paper “Multiscale simulations of complex systems by learning their effective dynamics” was published in Nature Machine Intelligence

Our paper “Modelling glioma progression, mass effect and intracranial pressure in patient anatomy” was published in the Journal of the Royal Society Interface

Our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” was published in Nature Communications

Archives

Calendar

January 2023
M	T	W	T	F	S	S
						1
2	3	4	5	6	7	8
9	10	11	12	13	14	15
16	17	18	19	20	21	22
23	24	25	26	27	28	29
30	31

« Dec

Address

Chair of Computational Science
ETH Zurich, CLT
Clausiusstrasse 33
CH-8092 Switzerland

+41 44 632 71 59
mavt-cse@mavt.ethz.ch

Recent Posts

Our paper “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100 000” was published in the Journal of Fluid Mechanics December 20, 2022
Our paper “Robust optimal sensor configuration using the value of information” was published in Structural Control and Health Monitoring November 10, 2022
Our paper “Multiscale simulations of complex systems by learning their effective dynamics” was published in Nature Machine Intelligence April 8, 2022
Our paper “Modelling glioma progression, mass effect and intracranial pressure in patient anatomy” was published in the Journal of the Royal Society Interface March 30, 2022
Our paper “Scientific multi-agent reinforcement learning for wall-models of turbulent flows” was published in Nature Communications March 19, 2022

Youtube