3 ms·

That would be really interesting - how do you generate the randomness? Every so often when Matlab or Simulink won't integrate something simple, probably becaus

That would be really interesting - how do you generate the randomness? Every so often when Matlab or Simulink won't integrate something simple, probably because of something I did, I wish I had an analog computer to compare to.

pjc50 5y ago
Randomness is impossible to eradicate from the analogue world, so you just take an existing noise source like a semiconductor junction and connect it to an amplifier. Some care is needed to get something that works across a wide temperature range, and avoids being easy to overwhelm with outside noise sources, but it's a problem with lots of existing solutions.
- buescher 5y ago
  I mean in a practical way for a monte carlo simulation - usually in analog setup you set parameters with discretes, so you are going to need to do things like feeding a transconductance amplifier with your noise source. You're also going to worry about shaping and otherwise parameterizing your distribution - do you need uniformly distributed noise? gaussian noise? what are your limits? I can imagine a lot of practical pitfalls and awkward half-solutions to trying to do an analog monte carlo; I was wondering how the OP went about it.
  - pvitz 5y ago
    There is actually a fascinating thesis by Yipeng Huang ("Hybrid Analog-Digital Co-Processing for Scientific Computation") that discusses toy models (i.e. Black-Scholes and variants). As usual, you have a Wiener process and thus need Gaussian noise. Yipeng Huang found that some noise stemming from a resistor ladder of the chip provided Gaussian white noise and he could control the mean by feeding it with a DAC and he also had some way to control the variance by changing some multipliers (but I can't tell you exactly how that worked). Nevertheless, this was the analog part and he faced issues with DC drift. Alternatively, he looked into generating the noise digitally with a microcontroller.
    - buescher 5y ago
      Thanks - the thesis does look interesting.
atoav 5y ago
Zener reverse avalanche noise + sample and hold?

pjc50 5y ago

Randomness is impossible to eradicate from the analogue world, so you just take an existing noise source like a semiconductor junction and connect it to an amplifier. Some care is needed to get something that works across a wide temperature range, and avoids being easy to overwhelm with outside noise sources, but it's a problem with lots of existing solutions.

buescher 5y ago
I mean in a practical way for a monte carlo simulation - usually in analog setup you set parameters with discretes, so you are going to need to do things like feeding a transconductance amplifier with your noise source. You're also going to worry about shaping and otherwise parameterizing your distribution - do you need uniformly distributed noise? gaussian noise? what are your limits? I can imagine a lot of practical pitfalls and awkward half-solutions to trying to do an analog monte carlo; I was wondering how the OP went about it.
- pvitz 5y ago
  There is actually a fascinating thesis by Yipeng Huang ("Hybrid Analog-Digital Co-Processing for Scientific Computation") that discusses toy models (i.e. Black-Scholes and variants). As usual, you have a Wiener process and thus need Gaussian noise. Yipeng Huang found that some noise stemming from a resistor ladder of the chip provided Gaussian white noise and he could control the mean by feeding it with a DAC and he also had some way to control the variance by changing some multipliers (but I can't tell you exactly how that worked). Nevertheless, this was the analog part and he faced issues with DC drift. Alternatively, he looked into generating the noise digitally with a microcontroller.
  - buescher 5y ago
    Thanks - the thesis does look interesting.

atoav 5y ago

Zener reverse avalanche noise + sample and hold?