Research with Prof. Whiteson
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Wierd Tracks: High-energy physics track reconstruction using symbolic regression.
Latest Reconstruction Results:
- 100-Track Cases
- v20260518_142036__train50_test50__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01
- v20260518_131139__train50_test50__layers25_len320p0__r3p1-53p0__fd5-5__func3-3__noiseXY0p01_Z0p01
- v20260518_142850__train50_test50__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01__standardModel_using_Phi
- v20260519_110640__train50_test50__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01__randomNoise
- True_Fakes_Levi_Train_SM+Schwartz_Set_19_Test_SM+Schwartz_Set_10
- 10-Track Cases
- v20260407_091434__train5_test5__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01__standardModel_using_Phi
- v20260305_002410__train5_test5__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01
- v20260430_193549__train5_test5__layers25_len320p0__r3p1-53p0__fd5-5__func3-3__noiseXY0p01_Z0p01__randomNoise
- v20260430_092453__train5_test5__layers25_len320p0__r3p1-53p0__fd5-5__func3-3__noiseXY0p01_Z0p01
- Legacy (unused)
- v20260407_091434__train5_test5__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01__standardModel
- v20260202_142140__train10_test10__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01__standardModel
- v20260122_163839__train10_test10__layers25_len320p0__r3p1-53p0__fd25-25__func3-3__noiseXY0p01_Z0p01
- v1_noiseless_69000
- track_results
Research with Prof. Carretero
- Interactive Pendulum Simulator
- Pendulum simulation gallery for \(\;\ddot{\theta} + \frac{g}{l} \sin (\theta) = \frac{A \omega}{l} \left( \cos (\theta) \omega \cos(\omega t) \right), \; \theta_0 = \frac{\pi}{6}, \; \dot{\theta}_0 = 0\)
- Control problem: guiding a particle to the top of hills (Most recent/comprehensive results; below are some older results)
- Control problem: Applet to view the Neural-Network control-function \(\xi(t)\) utilized in the "Control problem" studies in the bullet point right above this one
- Simulator for ODE (Particle) vs PDE (Bright Soliton) Dynamics (C++ 🚀)
- A deceiving display comparing ODE (Particle) vs PDE (Bright Soliton) center of mass trajectories for different initial velocities \( v_0 \), & also PDE conservation of "total mass"
Preprints & Publications
- Preprint: Generalized Fixed-Depth Prefix and Postfix Symbolic Regression Grammars
- Preprint: Solving the 2D Advection-Diffusion Equation using Fixed-Depth Symbolic Regression and Symbolic Differentiation without Expression Trees
- Preprint: Deducing Closed-Form Expressions for Bright-Solitons in Strongly Magnetized Plasmas with Physics Informed Symbolic Regression (PISR)
- AI2ASE Workshop Paper: Physics-Informed Symbolic Regression Discovers Simple Analytic Approximations for the Steady Swift–Hohenberg Equation in Polar Coordinates