- 241
- 49 271
Morten Hjorth-Jensen
United States
เข้าร่วมเมื่อ 16 ก.พ. 2012
Many-body physics: from FCI to Hartree-Fock theory
Many-body physics: from FCI to Hartree-Fock theory
มุมมอง: 16
วีดีโอ
Machine learning and optimization: Gradient descent, from simple gradient descent to momentum
มุมมอง 677 ชั่วโมงที่ผ่านมา
Lecture September 23, 2024
Many-body lectures, from the particle-hole formalism to Full Configuration Interaction theory
มุมมอง 2814 ชั่วโมงที่ผ่านมา
Many-body lectures, from the particle-hole formalism to Full Configuration Interaction theory
Particle-hole formalism, examples and diagrammatic representation
มุมมอง 5016 ชั่วโมงที่ผ่านมา
Particle-hole formalism, examples and diagrammatic representation
Machine learning course, exercises week 38 and discussion of cross-validation
มุมมอง 4021 ชั่วโมงที่ผ่านมา
Practicalities of cross-validation
Machine lecture on Logistic regression and Gradient methods
มุมมอง 78วันที่ผ่านมา
Basic intro to gradient methods and set up of optimization problem using Logistic Regression
Many-body physics lectures, begin particle-hole formalism
มุมมอง 29วันที่ผ่านมา
Introducing the particle-formalism with various examples
Many-body lecture September 12, discussion of Wick's theorem, examples, diagrammatic representations
มุมมอง 5214 วันที่ผ่านมา
Discussion of exercises for week 37
Machine learning course, exercises week 37
มุมมอง 8514 วันที่ผ่านมา
Discussion of statistical analysis of linear regression methods, bias-variance tradeoff and resampling techniques with code examples
Machine learning lecture FYS-STK3155/4155, September 9, 2024
มุมมอง 13814 วันที่ผ่านมา
Statistical interpreation of OLS, Ridge and Lasso regression
Many-body physics, Wick's theorem and diagrammatic representation, second part of lecture Sept. 6
มุมมอง 3814 วันที่ผ่านมา
Many-body physics, Wick's theorem and diagrammatic representation, second part of lecture Sept. 6
Many-body physics: Wick's theorem with examples, part 1 of lecture September 6
มุมมอง 3514 วันที่ผ่านมา
Many-body physics: Wick's theorem with examples, part 1 of lecture September 6
Many-body lectures FYS4480/9480, introducing Wick's theorem
มุมมอง 4321 วันที่ผ่านมา
Many-body lectures FYS4480/9480, introducing Wick's theorem
Machine learning course, Exercises week 36
มุมมอง 8221 วันที่ผ่านมา
Discussion of scaling and project 1
FYS-STK3155/4155 lecture September 2, Linear regression, OLS, Ridge and Lasso
มุมมอง 9221 วันที่ผ่านมา
Discussion and interpretation of Ridge, Lasso and ordinary least squares. Start discussion of statistical interpretations
Machine learning course, exercises week 35
มุมมอง 13628 วันที่ผ่านมา
Machine learning course, exercises week 35
First week of many-body lecture, introduction to basis sets and Hamiltonians
มุมมอง 34หลายเดือนก่อน
First week of many-body lecture, introduction to basis sets and Hamiltonians
Introduction to course on Many-Body Physics at the University of Oslo, Fall 2024
มุมมอง 73หลายเดือนก่อน
Introduction to course on Many-Body Physics at the University of Oslo, Fall 2024
Introduction to Machine learning course FYS-STK3155/4155 at the university of Oslo.
มุมมอง 188หลายเดือนก่อน
Introduction to Machine learning course FYS-STK3155/4155 at the university of Oslo.
Generative models, from VAEs to diffusion models, part 6
มุมมอง 724 หลายเดือนก่อน
Generative models, from VAEs to diffusion models, part 6
Generative models, from VAEs to GANS, part 5
มุมมอง 654 หลายเดือนก่อน
Generative models, from VAEs to GANS, part 5
Variational autoencoders, part 4, lecture April 23, 2024
มุมมอง 895 หลายเดือนก่อน
Variational autoencoders, part 4, lecture April 23, 2024
Variational Monte Carlo with fermions, efficient calculations of Slater determinants
มุมมอง 695 หลายเดือนก่อน
Variational Monte Carlo with fermions, efficient calculations of Slater determinants
Generative methods: From energy-based models to variational autoencoders, part 3
มุมมอง 875 หลายเดือนก่อน
Generative methods: From energy-based models to variational autoencoders, part 3
Deep learning and quantum mechanics, using Boltzmann machines in VMC calculations, Part 2
มุมมอง 1495 หลายเดือนก่อน
Deep learning and quantum mechanics, using Boltzmann machines in VMC calculations, Part 2
Clark Richard Moore Steven Taylor Jeffrey
Moore Mark Lewis Charles Lewis Timothy
Lewis Anthony Harris Elizabeth Hernandez Barbara
thank you for uploading!
Our pleasure!
Harris Richard Walker Linda Taylor Betty
Garcia Edward Allen Maria Taylor Scott
❣❣
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.