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Morten Hjorth-Jensen
United States
เข้าร่วมเมื่อ 16 ก.พ. 2012
Many-body perturbation theory, diagram examples and discussion of second midterm
Many-body perturbation theory, diagram examples and discussion of second midterm
มุมมอง: 8
วีดีโอ
Decision trees and ensemble methods. Introduction to decision trees. Part 1
มุมมอง 257 ชั่วโมงที่ผ่านมา
Decision trees and ensemble methods. Introduction to decision trees. Part 1
Many-body perturbation theory, discussion of diagrams and second midterm
มุมมอง 1514 ชั่วโมงที่ผ่านมา
Many-body perturbation theory, discussion of diagrams and second midterm
Many-body perturbation theory, diagram rules and examples
มุมมอง 2116 ชั่วโมงที่ผ่านมา
Many-body perturbation theory, diagram rules and examples
Many-body perturbation theory with examples, part 3
มุมมอง 29วันที่ผ่านมา
Many-body perturbation theory with examples, part 3
Many-body lectures, perturbation theory, part 2
มุมมอง 2814 วันที่ผ่านมา
Derivation of Brillouin-Wigner and Rayleigh-Schroedinger perturbation theory
Many-body perturbation theory, introduction
มุมมอง 4314 วันที่ผ่านมา
Start lecture series on many-body perturbation theory
Convolutional neural networks, part 1
มุมมอง 8914 วันที่ผ่านมา
Discussion of the basic mathematics of CNNs
Solving differential equations with neural networks
มุมมอง 12821 วันที่ผ่านมา
Part 4 of neural networks, applications to the solution of differential equations
Building a neural network code from scratch, part 3
มุมมอง 18921 วันที่ผ่านมา
Examples of simpler code and an object-oriented code plus TensorFlow examples
Many-body physics: energy of homogeneous electron gas in 3d and start density functional theory
มุมมอง 5228 วันที่ผ่านมา
Many-body physics: energy of homogeneous electron gas in 3d and start density functional theory
Additional material for project 2
มุมมอง 65หลายเดือนก่อน
Reminder on logistic regression and how to code it and how to present classification results.
Building a neural network code, part 2
มุมมอง 182หลายเดือนก่อน
Discussion of simpler cases and the back propagation algorithm and its implementation. Part 3, final code examples is discussed next week
Many-body physics, ground state energy of the electron gas
มุมมอง 31หลายเดือนก่อน
Many-body physics, ground state energy of the electron gas
Many-body physics: Hartree-Fock theory and the homogeneous electron gas in three dimensions
มุมมอง 64หลายเดือนก่อน
Many-body physics: Hartree-Fock theory and the homogeneous electron gas in three dimensions
Introduction to neural networks, building a code from scratch. Part 1
มุมมอง 196หลายเดือนก่อน
Introduction to neural networks, building a code from scratch. Part 1
Stability of Hartree-Fock solutions and Thouless' theorem
มุมมอง 46หลายเดือนก่อน
Stability of Hartree-Fock solutions and Thouless' theorem
Hartree-Fock theory, algorithm and Thouless' theorem and discussion of stability of HF equations
มุมมอง 69หลายเดือนก่อน
Hartree-Fock theory, algorithm and Thouless' theorem and discussion of stability of HF equations
Gradient descent methods, from plain GD to stochastic gradient descent and adaptive learning rates
มุมมอง 167หลายเดือนก่อน
Gradient descent methods, from plain GD to stochastic gradient descent and adaptive learning rates
Many-body lectures: basics of Hartree-Fock theory
มุมมอง 67หลายเดือนก่อน
Many-body lectures: basics of Hartree-Fock theory
Many-body physics: from FCI to Hartree-Fock theory
มุมมอง 46หลายเดือนก่อน
Many-body physics: from FCI to Hartree-Fock theory
Machine learning and optimization: Gradient descent, from simple gradient descent to momentum
มุมมอง 126หลายเดือนก่อน
Machine learning and optimization: Gradient descent, from simple gradient descent to momentum
Many-body lectures, from the particle-hole formalism to Full Configuration Interaction theory
มุมมอง 63หลายเดือนก่อน
Many-body lectures, from the particle-hole formalism to Full Configuration Interaction theory
Particle-hole formalism, examples and diagrammatic representation
มุมมอง 74หลายเดือนก่อน
Particle-hole formalism, examples and diagrammatic representation
Machine learning course, exercises week 38 and discussion of cross-validation
มุมมอง 49หลายเดือนก่อน
Machine learning course, exercises week 38 and discussion of cross-validation
Machine lecture on Logistic regression and Gradient methods
มุมมอง 94หลายเดือนก่อน
Machine lecture on Logistic regression and Gradient methods
Many-body physics lectures, begin particle-hole formalism
มุมมอง 512 หลายเดือนก่อน
Many-body physics lectures, begin particle-hole formalism
Many-body lecture September 12, discussion of Wick's theorem, examples, diagrammatic representations
มุมมอง 682 หลายเดือนก่อน
Many-body lecture September 12, discussion of Wick's theorem, examples, diagrammatic representations
Machine learning course, exercises week 37
มุมมอง 942 หลายเดือนก่อน
Machine learning course, exercises week 37
Machine learning lecture FYS-STK3155/4155, September 9, 2024
มุมมอง 1552 หลายเดือนก่อน
Machine learning lecture FYS-STK3155/4155, September 9, 2024
Thanks a lot for the class. I have a question. In order to follow the definition of the change of basis at 25:42, could the order of the subscripts at 37:40 be reversed?
Martin Kimberly Lopez Elizabeth Jones Jeffrey
Molto chiaro! Grazie mille!
Grazie mille e buon fine settimana
thank you for uploading!
Our pleasure!
❣❣
Thank you, hope you enjoyed the content Mohamed. You can find lecture notes and more at github.com/CompPhysics/ComputationalPhysics2
What about trying this on more difficult equations, e.g. Navier-Stokes??
Indeed, this is the plan, however, keep in mind that the present course is at the senior undergrad level and/or beginning master of science level, 3rd or 4th year of study and many students are not fully familiar with PDEs. For my advanced course, FYS5429 at the university of Oslo, the plan is indeed to do so, but then in connection with Physics informedn NN. You can find an example of such a project, with links to codes etc at github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/Projects/2023/ProjectExamples/PINNs.pdf
Wounderfull
Thank you so much!
Thx so much, and feel free to look up more at my github.com/mhjensen address. Best wishes
Oktay Sinanoğlu brought me here :)
Hope you'll find what you useful
Dear Professor Jensen, Could you please send me your email address? Thank you.
here it is, mhjensen@uio.no and sorry for the delay in answering
Good tutorial.
Glad you think so!. If you are interested in more such applications, in particular in connection with physics informed NN, see a project from 2023 for my advanced course FYS5429 at the university of Oslo, github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/Projects/2023/ProjectExamples/PINNs.pdf
It's a great lecture and I thing Morten is a great teacher who presents material slowly but understandably, so I am going to watch all of these lectures. However, there is one typo at the minute 52, namely, I think the bottom right matrix element |a_1|^* should be changed to |a_1|^2. The same is in the python notebook on github. Other than that this series of lectures is a very helpful introduction to the quantum computing literature.
Thx so much for your comments, they are much appreciated and thx for spotting these typos. I am updating the lecture notes during the summer and will add more material as well. A great thank you.
*Promo sm* 👀
hi sir there is no audio towards the end of the clip
Sorry for this indeed, I am going (this summer) to remake many of these videos, hopefully to the better. It is not always easy to have all of technologies collaborate while you record when teaching
The 2x2 Matrix for the Quantum states is cool 😊
Thank you!!
It was wonderful to find your TH-cam channel. We enjoyed reading your book on Computational Physics in pdf format. I never thought that I should search for you on TH-cam. Perhaps, this is a positive effect of the Covid pandemic when teachers came out and became more accessible. Thanks for sharing your videos here.
Welcome aboard! And thanks so much. I have also lots of teaching material on machine learning and quantum computing, with videos, just go to my github address at github.com/mhjensen
Wonderful. I shall certainly explore. Thanks once again!!@@MortenHjorthJensen
Thx a million for your kind words! These videos will be updated with more material this coming spring. Stay tuned
Morten Hjorth-Jensen, you are really cool, i've never seen so good explanation of everything. neural networks is the future!
Thanks you so much and feel free to use the material as you want. I have also lots of teaching material on machine learning and quantum computing, with videos, just go to my github address at github.com/mhjensen
First! )
Gonna be jumping on the bandwagon and proceed to give you my most sincere form of gratitude for these lectures. As a researcher with a foot in many fields at once, there comes a point when one can’t just keep up with every advance in every field one is paying attention to by just reading the literature or half-baked documentation. This is where skillful teaching really shines. I’ve only begun the lectures, but look forward to combing through them-- especially the PDE part. Cheers from the Caribbean 🏝️
This is exactly what I was hoping for. Your comments really warm and feel free to use the material for various courses as you want, see github.com/mhjensen for more educational material. Sorry for the late response, I had overseen several mails from youtube since I did not have a warning on.
What if the cost function is more complex ie dependent on the derivative of the output layer a^L? e.g. in energy minimisation
Hello can you add subtitle. Im so bad in english
It takes TH-cam some time to generate the subtitles, but they should be there now. Thx and best wishes.
Amazing lecture! I just wonder - when I have a nonzero source term on the right hand side of the ODE which is a function of X (for example shaft torque difference in jet engine RPM governing equation being function of time) and the source term depends on other variables (fuel flow, temperature, pressure), should the inputs od ANN include the other variables alongside with X variable? Becouse as far as I studied PINNs, they are used for self-evolving dynamic systems (zero or constant source terms, closed systems, Navier-Stokes, diffusion, convection etc.), where all that is needed to train the ANN consist of Y training data and outputs of the ANN itself (autograd derivatives and so on) and there is no need to use other measured data except the one output variable Y. But in the case of jet engine the right hand side - acceleration rate - is a function of time which must be known from data (measured or computed) in order to estimate ODE cost. Maybe I'm missing something, or can't PINNs really be used for modelling open thermodynamic systems? Thanks, Professor. EDIT: I mean the case, where the RHS is not only function of Y(X) but also other variables which are functions of X
🙌 "Promo sm"
Can I enroll in this course?
If you are a student at the university of Oslo, you can attend. If not, lectures are open via zoom, see the weekly plans and more at www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h23/index.html. This is the official website of the course, with links to schedule and weekly material as well as the GitHub address for all teaching material, see github.com/CompPhysics/MachineLearning and compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. Feel free to reach me at mhjensen@uio.no or hjensen@msu.edu
@@MortenHjorthJensen Thanks Sir.
Hard
nicely explained.
Thanks so much. Sorry for the late response, I had overseen several mails from youtube since I did not have a warning on.
I would like to pay my sincere thanks to Professor for your valuable lectures on Neural Network to solve differential equations. They helped me a lot in solving boundary layer problems
Thank you so much for your kind words. Best wishes
Promo SM
Thank you so much for this incredible lecture. Is there a TensorFlow or PyTorch version of these codes?
Thx so much for your kind comments. I will upload a tensor flow version as soon as possible. Feel free to send me an email to hjensen@msu.edu and I will send you a Jupyter-notebook.
lekker bezig wim.
Dear sir, Thank you very much for the valuable Video. I was looking for these examples since a long time.
Thank you very much and best wishes
What about CNN
Thx for the comment. Are you thinking of CNNs applied to the solution of differential equations? Else, if you are interested in solving differential equations with ML, I recommend strongly Brunton's and Kutz' recent text on data driven science and engineering.
much needed video!! 😍😍
Thx so much, and feel free to look up more at my github.com/mhjensen address. Best wishes and sorry for the late response, I had overseen several mails from youtube since I did not have a warning on.
Yes, for small equations it is working fine, but what about ODEs of CFD or FEA?
I would strongly recommend Steve Brunton's and Kutz' work on this, see their textbook and also a video here, th-cam.com/video/IXMSOSEj14Q/w-d-xo.html. This is indeed a very active and interesting research field. Highly recommended. They have, with colleagues, really been pioneering work on such topics.
Very helpful lecture
Thx so much, and feel free to look up more at my github.com/mhjensen address. Best wishes and sorry for the late response, I had overseen several mails from youtube since I did not have a warning on.
@@MortenHjorthJensen already fellow your GitHub thanks again and wish more progress dear professor
𝔭𝔯𝔬𝔪𝔬𝔰𝔪
Thank you for it ! can i get the code for advection equation using neural network?
Thx so much, and feel free to look up more at my github.com/mhjensen address. Best wishes and sorry for the late response, I had overseen several mails from youtube since I did not have a warning on. The codes are at github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week43/ipynb/week43.ipynb
greatly elucidated the basic concepts of ML.
Thx so much, and feel free to look up more at my github.com/mhjensen address. Best wishes and sorry for the late response, I had overseen several mails from youtube since I did not have a warning on.