Difference between Invariance and Covariance

In summary, Lorentz Invariance refers to physical quantities that do not change under a Lorentz transformation, while Lorentz Covariance refers to equations whose form is preserved by a change of coordinate system. An "invariant" quantity doesn't change when you change coordinate systems, while a "covariant" quantity changes homogeneously. An equation is "covariant" if it being true in one system implies it is true in every coordinate system, while an equation is "invariant" if it has exactly the same form in every coordinate system. Maxwell's equations are both Lorentz invariant and Lorentz covariant, with the values in the equations transforming and the form of the equations remaining the same. This was
  • #1
haaj86
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Hi, what is the difference between Lorentz Invariance and Lorentz Covariance?
From my lecture note (Group theory course) Invariance and Covariance where defined as follows:
Invariance: refers to the property of objects being left unchanged by symmetry operations.
Covariance: refers to equations whose form is preserved by a change of coordinate system.

From this I had the understanding that Lorentz Invariance refers to physical quantities that do not change under a Lorentz transformation and Lorentz covariance refers to equations that do not change after a coordinates transformations (of course the equation will be going from primed to unprimed after a Lorentz transformation but the structure of equations wouldn’t )
However, I have also read in physics books where the author talks about the invariance of “equations” just one example here “Lorentz Invariance of the Dirac equation”.
Is this because the above definitions are not as solid in physics as in mathematics, or is my understanding of what they meant is wrong, or is it because the authors are being sloppy?
 
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  • #2
Yes, an "invariant" quantity doesn't change when you change coordinate systems. But I would say that a "covariant" quantity is one that change homogeneously: the transformed coefficients are sums of products of the old coordinates and numbers that depend on the coordinate transformation. One important consequence of that is that if v is a covariant quantity and v= 0 in one coordinate system them v= 0 in every coordinate system. From that it follows that if you have an equation involving covariant quantities, say u= v with u and v both covariant, then if u= v in one coordinate system, then u-v= 0 in that coordinate system so u- v= 0 or u= v in any coordinate system. That is the idea of a "covariant equation".

But notice that "covariant equation" is not the same as "invariant equation"! An equation is "covariant" if it being true in one system implies it is true in every coordinate system. An equation is "invariant" if it has exactly the same form in every coordinate system. For example, Laplace's equation is "invariant" in Euclidean coordinate systems- that is translations and rotations only.
 
  • #3
Thank you for your reply,
HallsofIvy said:
But notice that "covariant equation" is not the same as "invariant equation"! An equation is "covariant" if it being true in one system implies it is true in every coordinate system. An equation is "invariant" if it has exactly the same form in every coordinate system. For example, Laplace's equation is "invariant" in Euclidean coordinate systems- that is translations and rotations only.

Ok, what do we say about Maxwell's equations, are they Lorentz invariant or Lorentz covariant? And can you please give me examples of Lorentz covariant equations in physics.
 
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  • #4
haaj86 said:
Ok, what do we say about Maxwell's equations, are they Lorentz invariant or Lorentz covariant? And can you please give me examples of Lorentz covariant equations in physics.

The Maxwell's equations are Lorentz covariant: the values in them transform. Its form is "invariant": the same in different reference frames. These equations include the EM fields (as a four-tensor) and the charge four-current. Their Lorentz covariance was first established by the French Academician H. Poincaré (1905), as well as the Lorentz covariance of the mechanical equations. In fact, H. Poincaré had advanced the principle of relativity and had built the relativistic theory before A. Einstein did. He found the main invariants of the realtivity theory - the interval and the field invariants (E*H and E^2-H^2). He showed that this all could be understood a four-geometrical features.

Bob.
 
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Thank you Bob, I really now understand what invariance means. But I have one remaining point regarding to covariance
Bob_for_short said:
The Maxwell's equations are Lorentz covariant: the values in them transform
Are you referring here to the fact that the E and B field themselves transforms between a pair of inertial frames? Or are you referring to the Lorentz transformations of the space and time variables of the Maxwell’s equations?

Regarding to what you said about Poincare, I have read it recently that he was the one to come up with the principle of relativity and it was three months before Einstein’s paper on the electrodynamics of moving bodies, and he called it “Lorentz’s principle of relativity” . It’s quite interesting how Einstein had to put all the pieces together, but I'm not sure if he was aware of POincare's work.
 
  • #6
haaj86 said:
Thank you Bob, I really now understand what invariance means. But I have one remaining point regarding to covariance

Are you referring here to the fact that the E and B field themselves transforms between a pair of inertial frames? Or are you referring to the Lorentz transformations of the space and time variables of the Maxwell’s equations?

Yes, E and B transform between a pair of inertial frames. The space and time coordinates X transform also between a pair of frames. The Lorentz transformations apply to all the variables while changing the reference frame. Some variables transform as components of a tensor (E and B) , some other do as components of a vector (space-time X, Momentum-energy P), some do as scalars or invariants (mass m and charge e). A Lorentz transformation always means calculation of the searched variables in a new reference frame if they are known in an old one.

Bob.
 

What is the difference between invariance and covariance?

Invariance refers to the property of a system or mathematical equation to remain unchanged when certain transformations or operations are applied to it. For example, a circle is invariant under rotations, meaning that no matter how you rotate it, it will still look the same. On the other hand, covariance refers to the tendency of two variables to change together. In other words, when one variable increases, the other tends to increase or decrease as well.

How are invariance and covariance related?

Invariance and covariance are two concepts that are closely related but are not the same. Invariance can be seen as a special case of covariance, where the two variables are perfectly correlated and change together in the same way. However, covariance can also exist between variables that are not invariant under certain transformations.

What is an example of invariance in science?

An example of invariance in science is the conservation of energy. According to the law of conservation of energy, the total energy in a closed system remains constant, even though energy can be transformed from one form to another. This means that the total energy is invariant under different transformations, such as changes in form or position.

How is the concept of invariance used in physics?

Invariance is a fundamental concept in physics and is used to describe the behavior of physical systems. For example, the laws of physics, such as Newton's laws of motion, are invariant under certain transformations, meaning that they remain the same regardless of the frame of reference. Invariance is also used in fields such as quantum mechanics and relativity to describe the behavior of particles and space-time.

What are the practical applications of covariance in statistics?

Covariance is a statistical measure used to describe the relationship between two variables. It has many practical applications, such as in finance to measure the risk and return of investment portfolios, in genetics to study the relationship between different traits, and in machine learning to determine the correlation between features and the target variable. It is also used in data analysis to identify patterns and trends in data sets.

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