# Uncategorized

## Genetic Process Automation

Determining optimal control settings for an industrial process can be challenging. For example, when there are interactions between the effects of the controls, adjusting one setting can require readjusting other settings. This article addresses the problem using genetic optimization. Continue reading…

## Function Inverting Neural Network

While it is straightforward to invert a function like y = mx to produce the inverse, x = y/m, some functions can’t be easily inverted. One such function is the cumulative distribution function (CDF) of the normal probability distribution, where neither the CDF nor the inverse CDF (quantile function) can be expressed in a closed form. This article […]

## Integrating Neural Net: Deriving the Normal Distribution CDF

This article presents a method for training a neural network to derive the integral of a function. The technique works not only with analytically-solvable integrals but also with integrals that do not have a closed-form solution and are typically solved by numerical methods. An example is the normal distribution’s cumulative density function (CDF). Continue reading

## inverse Physics-Informed Neural Network

Many relationships in physics, biology, chemistry, economics, engineering, etc., are defined by differential equations. In general, a differential equation (DE) describes how variables are affected by the rate of change of other variables. For instance, a DE explains how the position of a mass vibrating on spring changes with time in relation to the mass’s […]

## Can Reinforcement Learning Generalize Beyond Its Training? Part 2

Training With a Digital Twin Part 1 described a reinforcement learning system used to find the optimal control settings for a reflow oven used for soldering electronic components to a circuit board. Part 2 presents the details of the oven simulator used to accelerate the training process. Continue reading…

## Can Reinforcement Learning Generalize Beyond Its Training? Part 1

Part 1 explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a product as it transitions through the process. […]

## Reinforcement Learning: Training Environment Simulator (paper)

A previous post describes a reinforcement learning model trained to find the optimal control settings for a reflow oven that solders electronic components to a circuit board. The oven’s moving belt transports the product (i.e., the circuit board) through multiple heating zones. This process heats the product according to a temperature-time target profile required to […]

## Reinforcement Learning: A Case Study in Model Generalization (paper)

Can Reinforcement Learning generalize beyond its training? This paper explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a […]