this is the area I'm interested in! I'm only a freshman in college, so I was looking everywhere for experts who is doing research in this field. RUclips algorithm really works out for me this time
The hodgkin huxley approximation is huge for neuromorphic computing. linear approximations mean closed form solutions are applicable, reducing the number of calculations needed during spiking neural network inference. if you'd like to know more, please get in touch with me.
This is soo cool, I would love to see how this compares to more conventional RL methods on MDPs or POMDPs, or PINN approaches (maybe this can also be thought of as a PINN?), especially it would be interesting to compare the network sizes of various methods. I would be interested to help integrate this within Nvidia Isaac sim for virtual world models. I briefly played with and encountered Koopman analysis when I did a project with wavelet feature representations and I've also thought about this potential.
Thanks for your interests! The code is hosted on our project website: generalroboticslab.com/AutomatedGlobalAnalysis as well as GitHub: github.com/generalroboticslab/AutomatedGlobalAnalysis. We can access both of them. Can you try again?
this is the area I'm interested in! I'm only a freshman in college, so I was looking everywhere for experts who is doing research in this field. RUclips algorithm really works out for me this time
I don't have to understand all of it, to understand the beauty of it.
The hodgkin huxley approximation is huge for neuromorphic computing. linear approximations mean closed form solutions are applicable, reducing the number of calculations needed during spiking neural network inference. if you'd like to know more, please get in touch with me.
This is soo cool, I would love to see how this compares to more conventional RL methods on MDPs or POMDPs, or PINN approaches (maybe this can also be thought of as a PINN?), especially it would be interesting to compare the network sizes of various methods. I would be interested to help integrate this within Nvidia Isaac sim for virtual world models. I briefly played with and encountered Koopman analysis when I did a project with wavelet feature representations and I've also thought about this potential.
Great video!
Would this be applicable to the Einstein field equations by any chance?
Can you please fix the SSL on your website so that we could take a look at the code?
Thanks for your interests! The code is hosted on our project website: generalroboticslab.com/AutomatedGlobalAnalysis as well as GitHub: github.com/generalroboticslab/AutomatedGlobalAnalysis. We can access both of them. Can you try again?
@@boyuan_chen Works now, thank you!
0:24 fingerprints pattern...
dx/dt = f(t) = f(x)