10.0K
BSPartial derivatives —> measures how steeply one ascends/descends in the east/west, without moving north/south, or in the north/south, without moving east/west.
@bsumathdept
#Partial Derivative
Schauen Sie sich 450+ Reels-Videos über Partial Derivative von Menschen aus aller Welt an.
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LEHow to Find Partial Derivatives
#math #calculus #calculus3 #study #education #learn #highschool #college
@leonsmath
595.5K
MA✨️video’s Manim code Link in bio- easy, fully explained & customizable.
Differentiation Made Simple! ✨️
In mathematics, differentiation is the process of finding the derivative of a function — the measure of how fast something changes. 🚀 It’s the foundation of calculus, showing us the instantaneous rate of change and the slope of a curve at a point. From velocity in physics to growth rates in economics, differentiation is everywhere. 🌍
👉 Why it matters:
✅ Understand how curves behave at every point.
✅ Learn why derivatives = slopes of tangents.
✅ See how the limit definition connects secant lines to tangents.
✅ Build stronger intuition for solving real-world problems.
🌟 Whether you’re studying for AP Calculus (US), A-Level Maths (UK), IB Math, or university-level engineering & science courses, mastering differentiation will unlock higher-level math and problem-solving. Perfect for exam prep, competitive exams, and self-study.
💡 Differentiation is not just for exams — it’s the language of physics, engineering, computer science, data science, economics, and AI. Once you understand it, you see change everywhere!
🚀 If you’ve ever wondered “how fast is this changing right now?” — that’s differentiation in action.
#differentiation #calculus #learnmath #fyp #views #likeme #explore
What do you like most ?
@mathematisa
627.0K
MAMaster the CHAIN RULE in UNDER 7 Minutes!🤓Full video is on my YouTube channel, Math with Professor V.
Need help with Calculus or your other math classes? Look no further! Math with Professor V is to the rescue! I have full length video lectures and short form videos on my YouTube channel breaking down various units into fun, digestible topics. Sequences and series, integrals, learning differentiation techniques, and all other things Calculus related are explained on my youtube channel! New uploads daily, link in bio! Xoxo, Professor V
#mathwithprofessorv #mathvideos #calculus #ratesofchange #averagerateofchange #factoring #limits #partialderivative #integrals #integration #usubstitution #substitutionmethod #mathprofessor #youtuber #youtubemath #calculushelp #improperintegrals #calculustutorials #stem #calculusprofessor #studyingcalculus #integrationtutorials #finalexams #calculusstudents #calculuslectures #surfacearea #sequencesandseries #infiniteseries #surfaceofrevolution #absolutevalue
@mathwithprofessorv
550.2K
MAVisual representation of Partial Derivatives!
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Follow For More @mat.h_director
#trending #trendingnow #trend #trendingreels
#reelsinstagram #math #mathematics #matematicas #geometry #matematica #animatedmath #mathmemes #mathlovers💝 #solve #solvedifferent #solution #solvethis #mathteacher #motion #mathematician #mathgraphs #mathdirector #mathproblems #mathproblems #viralreels #viral #chaostheory #chaos #result #discrete
@mat.h_director
3.5M
ENCredit: @the.potential.dropout
Business majors trying to understand PDEs 😂
Partial differential equations (PDEs) are mathematical equations that involve multiple independent variables, their partial derivatives, and an unknown function. They are fundamental in describing a wide range of physical, chemical, and biological phenomena, such as heat conduction, fluid dynamics, electromagnetic fields, and quantum mechanics. Unlike ordinary differential equations, which deal with functions of a single variable, PDEs capture the behavior of systems influenced by several independent variables, making them essential tools in modeling real-world systems with spatial and temporal dimensions.
The solutions of PDEs can be challenging to find, as they often involve boundary and initial conditions that must be satisfied simultaneously. Analytical techniques, such as separation of variables, Fourier series, and Green’s functions, are commonly used to solve simpler PDEs. However, for complex equations, numerical methods like finite difference, finite element, and spectral methods are employed to approximate solutions. These computational approaches are particularly crucial in engineering and science, where exact solutions are rarely available.
PDEs are integral to modern applied mathematics and continue to be an area of active research. Advances in computational power and algorithms have expanded the ability to solve increasingly complex PDEs, leading to breakthroughs in fields like climate modeling, medical imaging, and machine learning. Their versatility and ability to describe dynamic systems make PDEs an enduring and indispensable tool in understanding and predicting the behavior of the natural world.
#engineering #engineeringmemes #fypシ #business #comedy #memes #viral #funny #engineers #lol #fun #video #trending #follow #engineer #university
@engineer__core
2.9K
NAThey are all available on Amazon!
#ode #pde #diffeq #differentialequations #viral
@nateramerbooks
23.4K
MA🔍📐 Derivación Implícita mediante Derivadas Parciales: Una Herramienta del Cálculo Multivariable
🧠 Uso de la fórmula \frac{dy}{dx} = -\frac{\partial F/\partial x}{\partial F/\partial y} para funciones definidas implícitamente.
Este contenido visual presenta el método de derivación implícita basado en derivadas parciales, un enfoque eficiente cuando la relación F(x,y) = 0 no permite despejar explícitamente la variable dependiente.
▶️ Fundamentación teórica:
Dada una ecuación F(x,y) = 0 que define a y como función implícita de x , y bajo condiciones de diferenciabilidad y que \partial F/\partial y \neq 0 , se tiene:
\frac{dy}{dx} = - \frac{\frac{\partial F}{\partial x}}{\frac{\partial F}{\partial y}}
Esta relación se deduce directamente del teorema de la función implícita y evita la necesidad de manipulación algebraica extensa.
⚙️ Aplicaciones prácticas:
➡️ Análisis de curvas definidas por ecuaciones no lineales en geometría analítica.
➡️ Optimización en problemas de economía y teoría de juegos con restricciones implícitas.
➡️ Modelado en termodinámica y mecánica donde las variables están acopladas.
➡️ Herramienta fundamental en métodos numéricos para resolver ecuaciones diferenciales.
📌 Ventaja metodológica:
Permite calcular derivadas incluso cuando la función y(x) no está expresada de forma explícita, ampliando el alcance del análisis matemático en situaciones complejas.
👨🔬 Dirigido a:
Estudiantes y profesionales de cálculo avanzado, matemáticas aplicadas, ingenierías y ciencias económicas que trabajan con modelos implícitos.
💬 ¿Has utilizado este método en lugar de la derivación implícita tradicional para simplificar algún cálculo en tus proyectos o investigaciones?
Comparte tu experiencia en los comentarios.
—
Comparte este recurso con quienes busquen profundizar en técnicas de derivación y aplicaciones del cálculo multivariable.
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#maths #mathstricks #calculus #derivatives #mathematics #mathematik #engeneering #university #epn #uce #aprende #aprendizaje #nivelacion #paratii #fypシ❤️💞❤️ #mathidea
@math.idea.ec
308.7K
THWe start from zero and I walk you through what’s even the concept of a partial differential equation. Numbers and formulas come in later episodes. Here, I build your initial intuition and the initial context of this beautiful subject in mathematics that studies changes. From heat transfer, fluids dynamics, electromagnetism, to quantum mechanics and the stock market, PDEs are everywhere. I hope you find this video and playlist of value to you. I will be posting more in the next weeks, months, and years. A lot of videos await. Maybe 100 of them? I’m serious when I say, I’m going to teach you the entire course for free because this is YouTube and this is what YouTubers do, be great and be helpful to every stressed out student out there. Much love. Enjoy the journey.
Sections of the video:
• Intro
• Examples of PDEs
• What courses you need to have learned to be ready to tackle PDEs
• Who needs PDEs?
__________________________________________________________________________
#partialdifferentialequation #differentialequations #crashcourse #review #teaching #learning #physics #maths #engineering #mechanicalengineering #uni #university #student #universitystudent #study
@the.potential.dropout
207.0K
MA✨️Code Link in bio- ✨️This formula is a 'cornerstone of Calculus', forming the basis for differentiation rules used in physics, engineering, computer science, and more.
If you’re a student of calculus, mathematics, IIT-JEE, university math, or just curious about how things are derived, this video is for you!
👨🏫 Learning math by derivations, not just formulas, gives you clarity, confidence, and problem-solving power.
#views #likeme #fyp #differentiation 💡 Comment below: Do you prefer only using formulas or also learning how they are derived?
@mathematisa
150.1K
CLMa perché ci va l'o-piccolo nei polinomi di Taylor? Spiegazione degli ordini di infinitesimo di una funzione per distinguere tra Derivabilità e Differenziabilità delle funzioni con i polinomi di Taylor!
#matematica #analisi2 #analisi1 #analisimatematica #analisi #derivate #Taylor #polinomiditaylor #sessioneesami #derivabilità #differenziabilità
@clearmath_
369.0K
ME🔹 Determinante 3D Negativo
En álgebra lineal, una transformación lineal f: V → V entre espacios vectoriales queda representada por una matriz siempre que fijemos una base de salida B1 y una base de llegada B2.
La notación M(f, B1, B2) indica la matriz de f en esas bases.
¿Cómo se construye?
Tomamos cada vector de la base de salida B1 = {v1, v2, v3}.
Calculamos su imagen: f(v1), f(v2), f(v3).
Expresamos cada una de esas imágenes en coordenadas respecto a la base de llegada B2.
Esas coordenadas se colocan como columnas en la matriz.
Por tanto, M(f, B1, B2) codifica en sus columnas las coordenadas de las imágenes de los vectores de la base inicial.
Si usamos la misma base tanto en salida como en llegada (por ejemplo la base canónica B de R³), la matriz M(f, B, B) nos da directamente los vectores transformados en el mismo sistema de referencia.
En el ejemplo que estamos visualizando:
El vector e1 (base del eje x) se mantiene.
El vector e3 (base del eje z) también se mantiene.
El vector e2 (base del eje y) cambia de sentido: de (0,1,0) pasa a (0,−1,0).
Esto genera una situación muy típica:
El paralelepípedo generado por {f(e1), f(e2), f(e3)} tiene el mismo volumen que el generado por {e1, e2, e3}.
Pero la orientación cambia: el orden cíclico de los vectores ya no respeta la regla de la mano derecha, sino que se invierte.
👉 El determinante de la matriz capta justo eso:
Si es positivo, la orientación de la base se preserva.
Si es negativo, la orientación se invierte.
En este caso, det(M(f,B,B)) < 0, lo que nos dice que la transformación conserva la magnitud del volumen pero invierte la orientación del espacio, como si aplicara una reflexión.
#mathematics #matematicas #determinant #physics #fisica
@merlinomath
✨ #Partial Derivative Entdeckungsleitfaden
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