Live and Learn with Winnie Winnie
I never bought into it, cause seemed like too much hassle, just another framework, which needed so much customization to be useful, but well I needed it this time. And when you learn why, don t judge me I m a Gen Z. I spent a few hours fighting local environment configs, API bugs, and container permissions, [ ]
We’ve sourced the multi-modal satellite data, cleaned it and created a tensor pipeline, and dissected the mathematical morality of imbalanced loss functions. Now we dive into the core. Load Data Config We load in the data we prepared in the previous post and bind different channels, create tensors, split data in traing test and [ ]
It’s been a minute since my last post on data acquisition. Between a much needed vacation trip to Italy and wrapping up my tap dance recital (because life exists outside of PyTorch!), sitting down to write about this project took a backseat, as this whole process is quite time consuming, believe it or not, [ ]
Did you know NASA JPL has a service called Horizons which lets you look at mission statistics, here we try to predict the orbit and compare with Artemis I stats. https://github.com/w-winnie/MLsandbox/blob/main/astro_sandbox/notebooks/artemis/artemis_orbital_sandbox1.ipynb Orbital Mechanics is.. complicated, atleast to model with one equation, here we break the trajectory into multiple parts, first…
In our previous posts, we reached the limits of both the classical and computational worlds. Traditional numerical solvers, like Finite Element Methods (FEM) or LSODA are rigorous, but they are computationally crippling. Simulating the hypersonic fluid dynamics of a re-entering spacecraft or forecasting global spatiotemporal weather anomalies can take supercomputers days or weeks. We need [ ]
In the previous posts, we marveled at the elegance of the classical toolkit. We used characteristic equations, matrix exponentials, and integral transforms to perfectly decode the trajectories of physical systems. But as we established, the classical engine has a fatal flaw: it demands a cooperative universe. When say an engineer models a spacecraft re entering [ ]
In last part, we learned how to solve isolated differential equations, a single pendulum swinging, a single spring oscillating. But physical reality is rarely isolated, and it is rarely smooth. What happens when a physical system experiences a violent, instantaneous shock, like a sudden thruster burn on a satellite? The continuous, well behaved polynomials we’ve [ ]
In Part I, we explored the geometric blueprints of differential equations. We looked at first order systems and how we can occasionally use clever substitutions to bypass the curse of nonlinearity. But the physical universe is rarely satisfied with first order changes. Gravity, electromagnetism, and orbital mechanics all rely on acceleration the rate at [ ]
Change is the only constant in life That s also how the universe works in a way and how we study it, how something changes with respect to another and differential equations is what we use to describe this. The universe is not a static photograph it is a continuously evolving engine. Whether we are modeling [ ]
What is a Loss Function? As an intuitive understanding loss would be predicted value actual value i.e. how much off is prediction to our actual target, most of you might be familiar with R^2 loss in high school which is what we often compute while fitting points to a line. Loss function is what [ ]