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ObedDzik authored Jun 5, 2024
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"# Preliminaries\n",
"\n",
"In this notebook we will work through the solution of inverse problems using machine learning techniques.\n",
"The goal is rather introductory, more complex networks and tweaking of code can lead to much better results that we present here. \n",
"\n",
"We assume to have a forward problem of the form\n",
"$$ A u + \\epsilon = d $$\n",
"where $A$ is the forward operator, $u$ is the model, $\\epsilon \\sim N(0, \\sigma^2 I)$ is the noise and $d$ is the observed data.\n",
"The goal is to recover $u$ from the noisy data $d$.\n",
"\n",
"Throughout this tutorial we use simple yet powerful examples.\n",
"The first example is the borehole tomography where the each datum is\n",
"$$ d_i = \\int_{{\\rm ray}_i} u(x,z) d\\ell = \\int_{\\Omega} u(x,z)\\, \\delta(x-x(p_i),z-z(p_i)) dx\\, dz$$\n",
"Here, $p_i$ is the ray path and $\\delta$ is the Dirac delta function.\n",
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