Dist Babble

 So, in my model, dimensions aren't these abstract things floating out there beyond our reach. Instead, they're tied to the flow of time itself. Imagine time as this primary stream, and dimensions are like different facets or aspects that emerge from how this stream interacts with itself. Think of it this way—each dimension isn't a separate universe or some hidden layer we can't see. They actually come about from how time interacts within itself. It's like different ripples or patterns in a pond, where each ripple length represents a different dimension. You know how some theories talk about dimensions as these curled-up or extra spaces we can't perceive directly? I think that misses the point. Dimensions should be integrated into how we experience reality. We live and move through these dimensions every day—they're not something separate or far-off. Some theories suggest that higher dimensions are like slices or layers of a bigger structure. I see it ...

 Consider in temporal physics the concept of "emergent space" suggests that space itself is not a static backdrop but something that arises dynamically due to underlying temporal flows. An object, then, is not just a collection of static spatial coordinates but is intimately tied to these temporal flows and the emergent space they produce. Space is dynamically generated from temporal flows. It's not a pre-existing stage where events play out but an emergent property of temporal processes. S(t) = (r_1(t),r_2(t),r_3(t)) An object is defined by the values that emerge from these temporal flows. These values include the spatial coordinates and the properties that the object exhibits. f ( t ) is a function that quantifies the temporal flow or rate at a specific moment t t t . It encapsulates how temporal dynamics evolve over time, influencing the emergent spatial dimensions S ( t ) . In the equation v ( t ) = f ( t ) S ( t ) ​ , f ( t ) acts as a divisor that scales the spati...

These equations collectively outline the behavior of fields in my model, emphasizing their temporal evolution across multiple spatial dimensions and their interaction with temporal flows and potential energy. They provide a comprehensive framework for understanding how fields manifest and evolve within multi-dimensios in Temporal Physics. phi(t, S(t)): This represents a field phi at a specific time t and its corresponding spatial configuration S(t). In temporal physics, fields can vary over time and across different spatial dimensions. T: The Temporal Flow Operator T is a function or operator that encapsulates how temporal flows (u, v, w) in different spatial dimensions (x, y, z) interact with the field phi. It's analogous to the Hamiltonian operator in traditional physics but adapted to account for the multi-dimensional nature of time in your model. hbar: This symbol (ħ) denotes the reduced Planck's constant, which appears in quantum mechanics and signifies the scale at which ...

 Temporal Flow Transformations: Let's define a transformation matrix L that affects both the spatial coordinates and the temporal flow rates: [r_1'(t), r_2'(t), r_3'(t)] = L × [r_1(t), r_2(t), r_3(t)] [u'(t), v'(t), w'(t)] = L × [u(t), v(t), w(t)] Where u(t), v(t), w(t) are the temporal flow rates in each dimension. Matrix L: L could be defined as: L = [ [γ, -βγu, -βγv, -βγw], [-βγu, 1+(γ-1)u^2, (γ-1)uv, (γ-1)uw], [-βγv, (γ-1)uv, 1+(γ-1)v^2, (γ-1)vw], [-βγw, (γ-1)uw, (γ-1)vw, 1+(γ-1)w^2] ] Where: γ = 1 / √(1 - β^2) β = v_rel / c_max v_rel is the relative velocity between frames c_max is the maximum allowed rate of temporal flow Transformed Temporal Dynamics: T' = L × T × L^T Where L^T is the transpose of L. Example Equations: For a "boost" along the x-direction: r_1' = γ(r_1 - βut) r_2' = r_2 r_3' = r_3 u' = γ(u - βr_1/t) v' = v w' = w Invariant Quantity: The invariant quantity in this framework might be: (r_1/u)^2 + (r_...

 Wave Particle Duality. In this innovative model of temporal physics, wave-particle duality is not seen as a paradox, but as a natural consequence of the behavior of temporal flows. Here's how the model explains this fundamental concept: Fundamental Basis: Everything in the universe, including what we perceive as particles and waves, emerges from underlying temporal flows and rates. Temporal Flow Dynamics: The model describes how these temporal flows can vary in intensity and distribution across space and time. Wave Behavior: When temporal flows are more spread out or uniform, they manifest as wave-like phenomena. This occurs at speeds below the speed of light and is characterized by the ability of these flows to interfere, diffract, and refract. Particle Behavior: As temporal flows approach the speed of light, they reach a maximum value where no additional information can be conveyed. At this point, the flows manifest as discrete, particle-like entities. Unified Explanation: Rathe...

Consider Temporal physics as modeling dimensionality not as ambient topology, but as transcribed from quantized temporal conveyance dynamics interacting under dimensional constraints.   Most conventional approaches seem overly fixated on mapping an ever-increasing number of spatial dimensions, without truly grasping the foundational role and quantifiable mechanics of how dimensionality itself emerges and interacts at the most primitive quantized scales. My model's core focus on rigorously deconstructing dimensions down to the most fundamental constituent of time as the primordial singularity gets to the heart of the matter. The common notions of space-time being somehow interchangeable or unified miss the deeper truth that space must be grounded in and emergent from temporal flows operating under specific dimensional constraints and interaction dynamics. Einstein's geometric perspective was a stepping stone, the mathematical formalisms of Minkowski spacetime and Hilbert's...

 Defining Subjective Experience, Let's represent subjective experience as a function SE that maps contextual information and temporality to a manifold of subjective states: SE = f(C, T) Where: C = Contextual information matrix (more on this below) T = Temporal variable representing the scale/duration Contextual Information Matrix (C): This could be represented as a multi-dimensional tensor encoding various contextual factors like sensory inputs, memory, cognitive models, environmental stimuli etc. C = [c1, c2, c3,...,cn] Where c1, c2, etc are different contextual elements from the physical, cognitive, social, cultural realms etc. Introducing a temporal weighting factor to the matrix components: C(t) = [c1(t), c2(t), ...cn(t)] Where ci(t) captures how the strength of each contextual factor evolves over time based on its revisitation history. Having a recursive updating rule: C(t+1) = f(C(t), processing_dynamics)Showing how the current context matrix C(t+1) is a function of the previ...