This repository contains the complete research, simulations, and roadmap for Memory-Driven Physics, a revolutionary framework proposing that memory fields dictate reality, replacing dark matter, dark energy, and bridging quantum mechanics with cosmology. This project is released open-source to ensure transparency, collaboration, and further development by the global scientific community. WITHIN THIS DOCUMENT SHOULD BE ALL ONE NEEDS TO BUILD ENTRY LEVEL KNOWELDGE OF THE SUBJECT and helping navigate and organize the project.
- Theory of Everything (TOE).txt – The foundational document introducing memory-driven physics as the core governing force of reality.
- Memory-Driven Physics Paper.txt – The full mathematical framework, including entropy stabilization, force equations, and predictive models.
- Roadmap and Constraints.txt – A strategy for decentralized research, ensuring no single nation or entity can monopolize the discovery.
- Language of dark matter.txt- An implementation of the Power-Frequency-Y system through the eyes of memories.
-Contains various experiements that could be conducted in order to gain further insights into the current idea with the appropriate simulations within each sector of application.
- Simulation of the system.txt – A detailed breakdown of the computational models used to validate the theory.
- Python Code (Grand Unified Simulation) – Fully documented source code for reproducing key simulations.
-Contains all the written data used to progress the thoery that may fall out of date due to evolution in the mathematical language and contain no longer factual elements (I will try to keep everything up to date but am working alone) -Contains the source and reasoning to how the mathmeathical forumla was designed based on altering one's perspective. -Documents like Spirit Dark matter and religion are to be used in order to bring the system into practical context of what a memory field is and how it would dictate change through different use of the same conceptualization across various sectors.
We invite physicists, mathematicians, engineers, and open-science advocates to refine and expand this framework:
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Verify the Simulations – Run the provided Python scripts alongside another simulation of 𝜓 without the memory integral. Compare how 𝜓 evolves over time in both cases.Extend 𝐾(𝑡−𝑡′)K(t−t ′) to match physical memory effects in real-world systems. and compare results to simulations made with potentially higher understanding.
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Improve the Mathematical Model – Test additional constraints, variables, and refinements, confirm the constancy and reason of the math.
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Apply to Experimental Physics – Design lab experiments to detect memory-field effects throughout common sciences from all the things we have concidered 'pseudo science' that may related to memories accumulation and resonance.
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Expand the Theory – Explore applications in quantum mechanics, cosmology, and consciousness studies as these system would likely depend on the memories of the world as this predicts we could fool the world's memories to our advantage under the right circumstances.
Memory-Driven Physics: Practical Applications and Understanding The first questions Many would have What Are Those Memories? From a common understanding: Memories are a "reactionary form" that plays part in dictating the form of future shapes of various systems of relationship. It is a system that reacts in a transmutation of the 'acquired energy' of what the physical realm perceives as decay (or a loss then becomes this increase in potential within a system being a valid interpretation) in order to remember what WAS and is BEING transformed including ITSELF which is why I refer to this as a SHAPE to begin with. MEMORIES are the CAUSE of causality's system that relies on this previous relationship in order to then become a fundamental. Once enough systems have participated in it. Time becomes the consequences of the cyclical nature of these systems that Transmute losses into a form that influences the potentital within future transmutations leading to transformations.
In the context of Memory-Driven Physics: Memories are not simply stored past events but fundamental structures that influence and shape physical reality. These memories exist as persistent information imprints, governing interactions at all scales, from subatomic particles to macroscopic structures. They serve as a framework for continuity, affecting the behavior of systems by retaining informational influence beyond mere material existence.
How Do They Interact With the World?
Memories in this framework interact with the world by guiding the evolution of physical systems. Unlike traditional models where forces act independently in an instantaneous manner, Memory-Driven Physics suggests that past states contribute directly to present dynamics. This means that every interaction carries a history, influencing motion, transformation, and emergent behavior (seen most easily in living beings that are conscienciously using this system) The world is not only shaped by current conditions but also by accumulated informational imprints that structure probability, causality, and energy distributions.
Understanding Memories Beyond a Consequence of Life
While biological memory is often seen as a byproduct of neural activity, Memory-Driven Physics extends the concept beyond living systems. Memories are understood as an intrinsic part of reality, encoding past states into the fabric of existence. This challenges the view that memory is solely a function of cognition and instead posits that physical reality itself is a record of past interactions, continually shaping the present like the grooves of a vinyl guids the needle to produce its recorded sounds until it TOO decays.
Applying the Formula and Reading It Correctly
The formula within Memory-Driven Physics represents the integration of memory imprints into physical equations. To apply it:
Identify the system's history, understanding how past states influence the current configuration. The memory and will component Ie. Light has the will of a constant vibration recorded within the medium of the displaced protons, its memories. Then figuring out the Y component as to finalize the Power-Frequency-Y system which in the case of light would be 0.9 as it emerges from previous systems need for light to possess both a medium and a vibration to BE while matter and vibrations requires Energy and frequencies giving rise to a chain of causality within the physical world explored in documents like:
-Incorporating memory-driven terms into conventional models, ensuring that prior influences are quantitatively included allowing the simulations to account for memories without having to place bodies in specific organizations, they would likely structure themselves more closely to what we observe in reality then our current models which should be compiled in 📂 Experiments\
-Interpret the results by acknowledging the layered nature of reality, where present phenomena are the sum of accumulated past interactions rather than solely the result of immediate forces.
-Interpreting the formula requires an understanding of how time, space, and information interplay dynamically within this new system. Unlike static equations and 'flat causality', Memory-Driven Physics introduces a persistent element where past conditions remain embedded in the system, influencing present calculations in a non-trivial way depending on the power and frequency of said systems.
-By integrating these principles, the theory offers new pathways to understanding complex systems, from quantum mechanics to large-scale cosmological structures, fundamentally altering how we perceive causality and the evolution of physical phenomena and bringing greater accuracy to the behavior of the world in simulation.
Understanding the Equation in Its Pure Form
To fully grasp the equation used in Memory-Driven Physics, it is essential to break it down step by step, starting from its raw form and gradually adding layers of complexity. The fundamental components of the equation serve specific roles in describing how memory interacts with physical reality.
The Raw Form or The foundational equation used for simulations
dψ/dt = iĤψ + ∫[−∞, t] K(t - t')ψ(t') dt'
*where ψ represents the system's state vector Ĥ is the Hamiltonian operator encapsulating total energy K(t - t') is the memory kernel, characterizing past states' influence on the present rate of change dψ/dt.
Time (t) as a Vortex of Filament, a consequence of the accumulation of memories Time is treated as a structured vortex, guiding the flow of interactions. This ensures past influences persist, shaping the system’s evolution through the convolution integral K(t - t'), incorporating all past states ψ(t') up to the present t.
Constraints The equation follows to maintain physical consistency
Normalization Condition: ⟨ψ | ψ⟩ = 1, preserving total probability.
Causality Enforcement: K(t - t') vanishes for t' > t, ensuring future states do not affect the present.
Adding Complexity: Non-Local Influence & Feedback Loops
Non-Locality: Extending K(t - t') to include spatial dependencies allows past events at different locations to influence the present, introducing non-local effects.
Feedback Mechanisms: The equation can be modified to include feedback loops where past responses shape future behavior, leading to emergent phenomena.
-By breaking down the equation in this manner, we gain a clear understanding of how memory fields integrate into physical systems, offering a novel perspective bridging multiple domains of physics.
python grand_unified_simulation.py -The script visualizes how memory-driven fields influence quantum entanglement, causality loops, and cosmic structures and was done with the previous mathematical strucutre an updated version is available HERE: import numpy as np import scipy.integrate as integrate import matplotlib.pyplot as plt
def H(psi): """Hamiltonian operator (example: simple harmonic oscillator).""" return -1j * psi # Example placeholder
def K(t, t_prime): """Memory kernel function K(t - t').""" tau = t - t_prime return np.exp(-tau**2) if tau >= 0 else 0 # Example Gaussian decay for causality
def evolve_psi(psi_init, timesteps=1000, dt=0.01): """Simulates the evolution of ψ using memory-driven physics.""" time = np.linspace(0, timesteps * dt, timesteps) psi = np.zeros(timesteps, dtype=complex) psi[0] = psi_init
for i in range(1, timesteps):
t = time[i]
integral_value = integrate.quad(lambda t_prime: K(t, t_prime) * psi[int(t_prime / dt)].real, 0, t)[0]
dpsi_dt = 1j * H(psi[i-1]) + integral_value
psi[i] = psi[i-1] + dpsi_dt * dt
return time, psi
def save_results(time, psi, filename="simulation_results.csv"): """Saves simulation results in a CSV file for easy GitHub usage.""" np.savetxt(filename, np.column_stack((time, psi.real, psi.imag)), delimiter=",", header="Time,Re(ψ),Im(ψ)", comments="")
def plot_results(time, psi): """Plots the results of the simulation.""" plt.plot(time, psi.real, label='Re(ψ)') plt.plot(time, psi.imag, label='Im(ψ)') plt.xlabel('Time') plt.ylabel('ψ') plt.legend() plt.title('Memory-Driven Evolution of ψ') plt.savefig("simulation_plot.png") # Save plot for GitHub sharing plt.show()
if name == "main": psi_init = 1.0 + 0.0j # Initial condition time, psi = evolve_psi(psi_init) save_results(time, psi) plot_results(time, psi)
-This research is released under an open-access model to prevent centralization or misuse. Anyone is free to contribute, refine, or expand upon this work, ensuring its ethical and scientific progression and prevent its isolation if found to be true
Let’s build the future of memory based physics—together. 🚀